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GitHub - unit8co/darts: A python library for user-friendly forecasting and anomaly detection on time series.
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seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header Overlay-header--divided"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="custom-scopes-dialog-title"> Saved searches </h1> <h2 id="custom-scopes-dialog-description" class="Overlay-description">Use saved searches to filter your results more quickly</h2> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="custom-scopes-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="custom-scopes-dialog-title"> <div data-view-component="true" class="Overlay-body"> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <div hidden class="create-custom-scope-form" data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" 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src=\"https://camo.githubusercontent.com/5bf9e9fa18966df7cb5fac7715bef6b72df15e01a6efa9d616c83f9fcb527fe2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f64652532307374796c652d626c61636b2d3030303030302e737667\" alt=\"Code style: black\" data-canonical-src=\"https://img.shields.io/badge/code%20style-black-000000.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://gitter.im/u8darts/darts?utm_source=badge\u0026amp;utm_medium=badge\u0026amp;utm_campaign=pr-badge\u0026amp;utm_content=badge\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/45778b68a9cff427c6ca7e56ce613b0700ef6393b4aef417d8e4838e5629f7a4/68747470733a2f2f6261646765732e6769747465722e696d2f753864617274732f64617274732e737667\" alt=\"Join the chat at https://gitter.im/u8darts/darts\" data-canonical-src=\"https://badges.gitter.im/u8darts/darts.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDarts\u003c/strong\u003e is a Python library for user-friendly forecasting and anomaly detection\non time series. It contains a variety of models, from classics such as ARIMA to\ndeep neural networks. The forecasting models can all be used in the same way,\nusing \u003ccode\u003efit()\u003c/code\u003e and \u003ccode\u003epredict()\u003c/code\u003e functions, similar to scikit-learn.\nThe library also makes it easy to backtest models,\ncombine the predictions of several models, and take external data into account.\nDarts supports both univariate and multivariate time series and models.\nThe ML-based models can be trained on potentially large datasets containing multiple time\nseries, and some of the models offer a rich support for probabilistic forecasting.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eDarts also offers extensive anomaly detection capabilities.\nFor instance, it is trivial to apply PyOD models on time series to obtain anomaly scores,\nor to wrap any of Darts forecasting or filtering models to obtain fully\nfledged anomaly detection models.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://unit8co.github.io/darts/quickstart/00-quickstart.html\" rel=\"nofollow\"\u003eQuickstart\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://unit8co.github.io/darts/userguide.html\" rel=\"nofollow\"\u003eUser Guide\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.html\" rel=\"nofollow\"\u003eAPI Reference\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://unit8co.github.io/darts/examples.html\" rel=\"nofollow\"\u003eExamples\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHigh Level Introductions\u003c/h5\u003e\u003ca id=\"user-content-high-level-introductions\" class=\"anchor\" aria-label=\"Permalink: High Level Introductions\" href=\"#high-level-introductions\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://medium.com/unit8-machine-learning-publication/darts-time-series-made-easy-in-python-5ac2947a8878\" rel=\"nofollow\"\u003eIntroductory Blog Post\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://youtu.be/g6OXDnXEtFA\" rel=\"nofollow\"\u003eIntroduction video (25 minutes)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eArticles on Selected Topics\u003c/h5\u003e\u003ca id=\"user-content-articles-on-selected-topics\" class=\"anchor\" aria-label=\"Permalink: Articles on Selected Topics\" href=\"#articles-on-selected-topics\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://medium.com/unit8-machine-learning-publication/training-forecasting-models-on-multiple-time-series-with-darts-dc4be70b1844\" rel=\"nofollow\"\u003eTraining Models on Multiple Time Series\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://medium.com/unit8-machine-learning-publication/time-series-forecasting-using-past-and-future-external-data-with-darts-1f0539585993\" rel=\"nofollow\"\u003eUsing Past and Future Covariates\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4\" rel=\"nofollow\"\u003eTemporal Convolutional Networks and Forecasting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://medium.com/unit8-machine-learning-publication/probabilistic-forecasting-in-darts-e88fbe83344e\" rel=\"nofollow\"\u003eProbabilistic Forecasting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://medium.com/unit8-machine-learning-publication/transfer-learning-for-time-series-forecasting-87f39e375278\" rel=\"nofollow\"\u003eTransfer Learning for Time Series Forecasting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://medium.com/unit8-machine-learning-publication/hierarchical-forecast-reconciliation-with-darts-8b4b058bb543\" rel=\"nofollow\"\u003eHierarchical Forecast Reconciliation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eQuick Install\u003c/h2\u003e\u003ca id=\"user-content-quick-install\" class=\"anchor\" aria-label=\"Permalink: Quick Install\" href=\"#quick-install\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe recommend to first setup a clean Python environment for your project with Python 3.9+ using your favorite tool\n(\u003ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html\" title=\"conda-env\" rel=\"nofollow\"\u003econda\u003c/a\u003e,\n\u003ca href=\"https://docs.python.org/3/library/venv.html\" rel=\"nofollow\"\u003evenv\u003c/a\u003e, \u003ca href=\"https://virtualenv.pypa.io/en/latest/\" rel=\"nofollow\"\u003evirtualenv\u003c/a\u003e with\nor without \u003ca href=\"https://virtualenvwrapper.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003evirtualenvwrapper\u003c/a\u003e).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOnce your environment is set up you can install darts using pip:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"pip install darts\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epip install darts\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor more details you can refer to our\n\u003ca href=\"https://github.com/unit8co/darts/blob/master/INSTALL.md\"\u003einstallation instructions\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExample Usage\u003c/h2\u003e\u003ca id=\"user-content-example-usage\" class=\"anchor\" aria-label=\"Permalink: Example Usage\" href=\"#example-usage\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eForecasting\u003c/h3\u003e\u003ca id=\"user-content-forecasting\" class=\"anchor\" aria-label=\"Permalink: Forecasting\" href=\"#forecasting\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCreate a \u003ccode\u003eTimeSeries\u003c/code\u003e object from a Pandas DataFrame, and split it in train/validation series:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import pandas as pd\nfrom darts import TimeSeries\n\n# Read a pandas DataFrame\ndf = pd.read_csv(\u0026quot;AirPassengers.csv\u0026quot;, delimiter=\u0026quot;,\u0026quot;)\n\n# Create a TimeSeries, specifying the time and value columns\nseries = TimeSeries.from_dataframe(df, \u0026quot;Month\u0026quot;, \u0026quot;#Passengers\u0026quot;)\n\n# Set aside the last 36 months as a validation series\ntrain, val = series[:-36], series[-36:]\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epandas\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epd\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edarts\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTimeSeries\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Read a pandas DataFrame\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epd\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eread_csv\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"AirPassengers.csv\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edelimiter\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\",\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Create a TimeSeries, specifying the time and value columns\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eseries\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTimeSeries\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efrom_dataframe\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"Month\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"#Passengers\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Set aside the last 36 months as a validation series\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003etrain\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eval\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eseries\u003c/span\u003e[:\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e36\u003c/span\u003e], \u003cspan class=\"pl-s1\"\u003eseries\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e36\u003c/span\u003e:]\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFit an exponential smoothing model, and make a (probabilistic) prediction over the validation series' duration:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from darts.models import ExponentialSmoothing\n\nmodel = ExponentialSmoothing()\nmodel.fit(train)\nprediction = model.predict(len(val), num_samples=1000)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edarts\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodels\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eExponentialSmoothing\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eExponentialSmoothing\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efit\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etrain\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eprediction\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003epredict\u003c/span\u003e(\u003cspan class=\"pl-en\"\u003elen\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eval\u003c/span\u003e), \u003cspan class=\"pl-s1\"\u003enum_samples\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1000\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlot the median, 5th and 95th percentiles:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import matplotlib.pyplot as plt\n\nseries.plot()\nprediction.plot(label=\u0026quot;forecast\u0026quot;, low_quantile=0.05, high_quantile=0.95)\nplt.legend()\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ematplotlib\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003epyplot\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eplt\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003eseries\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eplot\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003eprediction\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eplot\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elabel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"forecast\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elow_quantile\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.05\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ehigh_quantile\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.95\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eplt\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003elegend\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/unit8co/darts/raw/master/static/images/example.png\"\u003e\u003cimg src=\"https://github.com/unit8co/darts/raw/master/static/images/example.png\" alt=\"darts forecast example\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAnomaly Detection\u003c/h3\u003e\u003ca id=\"user-content-anomaly-detection\" class=\"anchor\" aria-label=\"Permalink: Anomaly Detection\" href=\"#anomaly-detection\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eLoad a multivariate series, trim it, keep 2 components, split train and validation sets:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from darts.datasets import ETTh2Dataset\n\nseries = ETTh2Dataset().load()[:10000][[\u0026quot;MUFL\u0026quot;, \u0026quot;LULL\u0026quot;]]\ntrain, val = series.split_before(0.6)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edarts\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edatasets\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eETTh2Dataset\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003eseries\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eETTh2Dataset\u003c/span\u003e().\u003cspan class=\"pl-c1\"\u003eload\u003c/span\u003e()[:\u003cspan class=\"pl-c1\"\u003e10000\u003c/span\u003e][[\u003cspan class=\"pl-s\"\u003e\"MUFL\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"LULL\"\u003c/span\u003e]]\n\u003cspan class=\"pl-s1\"\u003etrain\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eval\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eseries\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003esplit_before\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e0.6\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBuild a k-means anomaly scorer, train it on the train set\nand use it on the validation set to get anomaly scores:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from darts.ad import KMeansScorer\n\nscorer = KMeansScorer(k=2, window=5)\nscorer.fit(train)\nanom_score = scorer.score(val)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edarts\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ead\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eKMeansScorer\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003escorer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eKMeansScorer\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ek\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ewindow\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003escorer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efit\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etrain\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eanom_score\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003escorer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003escore\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eval\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBuild a binary anomaly detector and train it over train scores,\nthen use it over validation scores to get binary anomaly classification:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from darts.ad import QuantileDetector\n\ndetector = QuantileDetector(high_quantile=0.99)\ndetector.fit(scorer.score(train))\nbinary_anom = detector.detect(anom_score)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edarts\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ead\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eQuantileDetector\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003edetector\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eQuantileDetector\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ehigh_quantile\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.99\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003edetector\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efit\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003escorer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003escore\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etrain\u003c/span\u003e))\n\u003cspan class=\"pl-s1\"\u003ebinary_anom\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edetector\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003edetect\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eanom_score\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlot (shifting and scaling some of the series\nto make everything appear on the same figure):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import matplotlib.pyplot as plt\n\nseries.plot()\n(anom_score / 2. - 100).plot(label=\u0026quot;computed anomaly score\u0026quot;, c=\u0026quot;orangered\u0026quot;, lw=3)\n(binary_anom * 45 - 150).plot(label=\u0026quot;detected binary anomaly\u0026quot;, lw=4)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ematplotlib\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003epyplot\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eplt\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003eseries\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eplot\u003c/span\u003e()\n(\u003cspan class=\"pl-s1\"\u003eanom_score\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e2.\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e100\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003eplot\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elabel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"computed anomaly score\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ec\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"orangered\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elw\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e)\n(\u003cspan class=\"pl-s1\"\u003ebinary_anom\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e45\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e150\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003eplot\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elabel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"detected binary anomaly\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003elw\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/unit8co/darts/raw/master/static/images/example_ad.png\"\u003e\u003cimg src=\"https://github.com/unit8co/darts/raw/master/static/images/example_ad.png\" alt=\"darts anomaly detection example\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eForecasting Models:\u003c/strong\u003e A large collection of forecasting models; from statistical models (such as\nARIMA) to deep learning models (such as N-BEATS). See \u003ca href=\"#forecasting-models\"\u003etable of models below\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAnomaly Detection\u003c/strong\u003e The \u003ccode\u003edarts.ad\u003c/code\u003e module contains a collection of anomaly scorers,\ndetectors and aggregators, which can all be combined to detect anomalies in time series.\nIt is easy to wrap any of Darts forecasting or filtering models to build\na fully fledged anomaly detection model that compares predictions with actuals.\nThe \u003ccode\u003ePyODScorer\u003c/code\u003e makes it trivial to use PyOD detectors on time series.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMultivariate Support:\u003c/strong\u003e \u003ccode\u003eTimeSeries\u003c/code\u003e can be multivariate - i.e., contain multiple time-varying\ndimensions/columns instead of a single scalar value. Many models can consume and produce multivariate series.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMultiple Series Training (Global Models):\u003c/strong\u003e All machine learning based models (incl. all neural networks)\nsupport being trained on multiple (potentially multivariate) series. This can scale to large datasets too.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eProbabilistic Support:\u003c/strong\u003e \u003ccode\u003eTimeSeries\u003c/code\u003e objects can (optionally) represent stochastic\ntime series; this can for instance be used to get confidence intervals, and many models support different\nflavours of probabilistic forecasting (such as estimating parametric distributions or quantiles).\nSome anomaly detection scorers are also able to exploit these predictive distributions.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eConformal Prediction Support:\u003c/strong\u003e Our conformal prediction models allow to generate probabilistic forecasts with\ncalibrated quantile intervals for any pre-trained global forecasting model.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePast and Future Covariates Support:\u003c/strong\u003e Many models in Darts support past-observed and/or future-known\ncovariate (external data) time series as inputs for producing forecasts.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eStatic Covariates Support:\u003c/strong\u003e In addition to time-dependent data, \u003ccode\u003eTimeSeries\u003c/code\u003e can also contain\nstatic data for each dimension, which can be exploited by some models.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eHierarchical Reconciliation:\u003c/strong\u003e Darts offers transformers to perform reconciliation.\nThese can make the forecasts add up in a way that respects the underlying hierarchy.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRegression Models:\u003c/strong\u003e It is possible to plug-in any scikit-learn compatible model\nto obtain forecasts as functions of lagged values of the target series and covariates.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTraining with Sample Weights:\u003c/strong\u003e All global models support being trained with sample weights. They can be\napplied to each observation, forecasted time step and target column.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eForecast Start Shifting:\u003c/strong\u003e All global models support training and prediction on a shifted output window.\nThis is useful for example for Day-Ahead Market forecasts, or when the covariates (or target series) are reported\nwith a delay.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eExplainability:\u003c/strong\u003e Darts has the ability to \u003cem\u003eexplain\u003c/em\u003e some forecasting models using Shap values.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eData Processing:\u003c/strong\u003e Tools to easily apply (and revert) common transformations on\ntime series data (scaling, filling missing values, differencing, boxcox, ...)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eMetrics:\u003c/strong\u003e A variety of metrics for evaluating time series' goodness of fit;\nfrom R2-scores to Mean Absolute Scaled Error.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eBacktesting:\u003c/strong\u003e Utilities for simulating historical forecasts, using moving time windows.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePyTorch Lightning Support:\u003c/strong\u003e All deep learning models are implemented using PyTorch Lightning,\nsupporting among other things custom callbacks, GPUs/TPUs training and custom trainers.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFiltering Models:\u003c/strong\u003e Darts offers three filtering models: \u003ccode\u003eKalmanFilter\u003c/code\u003e, \u003ccode\u003eGaussianProcessFilter\u003c/code\u003e,\nand \u003ccode\u003eMovingAverageFilter\u003c/code\u003e, which allow to filter time series, and in some cases obtain probabilistic\ninferences of the underlying states/values.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDatasets\u003c/strong\u003e The \u003ccode\u003edarts.datasets\u003c/code\u003e submodule contains some popular time series datasets for rapid\nand reproducible experimentation.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eCompatibility with Multiple Backends:\u003c/strong\u003e \u003ccode\u003eTimeSeries\u003c/code\u003e objects can be created from and exported to various backends such as pandas, polars, numpy, pyarrow, xarray, and more, facilitating seamless integration with different data processing libraries.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eForecasting Models\u003c/h2\u003e\u003ca id=\"user-content-forecasting-models\" class=\"anchor\" aria-label=\"Permalink: Forecasting Models\" href=\"#forecasting-models\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eHere's a breakdown of the forecasting models currently implemented in Darts. We are constantly working\non bringing more models and features.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eModel\u003c/th\u003e\n\u003cth\u003eSources\u003c/th\u003e\n\u003cth\u003eTarget Series Support:\u003cbr\u003e\u003cbr\u003eUnivariate/\u003cbr\u003eMultivariate\u003c/th\u003e\n\u003cth\u003eCovariates Support:\u003cbr\u003e\u003cbr\u003ePast-observed/\u003cbr\u003eFuture-known/\u003cbr\u003eStatic\u003c/th\u003e\n\u003cth\u003eProbabilistic Forecasting:\u003cbr\u003e\u003cbr\u003eSampled/\u003cbr\u003eDistribution Parameters\u003c/th\u003e\n\u003cth\u003eTraining \u0026amp; Forecasting on Multiple Series\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eBaseline Models\u003c/strong\u003e\u003cbr\u003e(\u003ca href=\"https://unit8co.github.io/darts/userguide/covariates.html#local-forecasting-models-lfms\" rel=\"nofollow\"\u003eLocalForecastingModel\u003c/a\u003e)\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMean\" rel=\"nofollow\"\u003eNaiveMean\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e🔴 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal\" rel=\"nofollow\"\u003eNaiveSeasonal\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e🔴 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveDrift\" rel=\"nofollow\"\u003eNaiveDrift\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e🔴 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMovingAverage\" rel=\"nofollow\"\u003eNaiveMovingAverage\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e🔴 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eStatistical / Classic Models\u003c/strong\u003e\u003cbr\u003e(\u003ca href=\"https://unit8co.github.io/darts/userguide/covariates.html#local-forecasting-models-lfms\" rel=\"nofollow\"\u003eLocalForecastingModel\u003c/a\u003e)\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.arima.html#darts.models.forecasting.arima.ARIMA\" rel=\"nofollow\"\u003eARIMA\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 ✅ 🔴\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.varima.html#darts.models.forecasting.varima.VARIMA\" rel=\"nofollow\"\u003eVARIMA\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e🔴 ✅\u003c/td\u003e\n\u003ctd\u003e🔴 ✅ 🔴\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_arima.html#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA\" rel=\"nofollow\"\u003eStatsForecastAutoArima\u003c/a\u003e (faster AutoARIMA)\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast\"\u003eNixtla's statsforecast\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 ✅ 🔴\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.exponential_smoothing.html#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing\" rel=\"nofollow\"\u003eExponentialSmoothing\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ets.html#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS\" rel=\"nofollow\"\u003eStatsforecastAutoETS\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast\"\u003eNixtla's statsforecast\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 ✅ 🔴\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ces.html#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES\" rel=\"nofollow\"\u003eStatsforecastAutoCES\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast\"\u003eNixtla's statsforecast\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.BATS\" rel=\"nofollow\"\u003eBATS\u003c/a\u003e and \u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.TBATS\" rel=\"nofollow\"\u003eTBATS\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://robjhyndman.com/papers/ComplexSeasonality.pdf\" rel=\"nofollow\"\u003eTBATS paper\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_tbats.html#darts.models.forecasting.sf_auto_tbats.StatsForecastAutoTBATS\" rel=\"nofollow\"\u003eStatsForecastAutoTBATS\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast\"\u003eNixtla's statsforecast\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.Theta\" rel=\"nofollow\"\u003eTheta\u003c/a\u003e and \u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.FourTheta\" rel=\"nofollow\"\u003eFourTheta\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://robjhyndman.com/papers/Theta.pdf\" rel=\"nofollow\"\u003eTheta\u003c/a\u003e \u0026amp; \u003ca href=\"https://github.com/Mcompetitions/M4-methods/blob/master/4Theta%20method.R\"\u003e4 Theta\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_theta.html#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta\" rel=\"nofollow\"\u003eStatsForecastAutoTheta\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/Nixtla/statsforecast\"\u003eNixtla's statsforecast\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet\" rel=\"nofollow\"\u003eProphet\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/facebook/prophet\"\u003eProphet repo\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 ✅ 🔴\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.fft.html#darts.models.forecasting.fft.FFT\" rel=\"nofollow\"\u003eFFT\u003c/a\u003e (Fast Fourier Transform)\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.kalman_forecaster.html#darts.models.forecasting.kalman_forecaster.KalmanForecaster\" rel=\"nofollow\"\u003eKalmanForecaster\u003c/a\u003e using the Kalman filter and N4SID for system identification\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://people.duke.edu/~hpgavin/SystemID/References/VanOverschee-Automatica-1994.pdf\" rel=\"nofollow\"\u003eN4SID paper\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e🔴 ✅ 🔴\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.croston.html#darts.models.forecasting.croston.Croston\" rel=\"nofollow\"\u003eCroston\u003c/a\u003e method\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGlobal Baseline Models\u003c/strong\u003e\u003cbr\u003e(\u003ca href=\"https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms\" rel=\"nofollow\"\u003eGlobalForecastingModel\u003c/a\u003e)\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveAggregate\" rel=\"nofollow\"\u003eGlobalNaiveAggregate\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e🔴 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveDrift\" rel=\"nofollow\"\u003eGlobalNaiveDrift\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e🔴 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveSeasonal\" rel=\"nofollow\"\u003eGlobalNaiveSeasonal\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e🔴 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e🔴 🔴\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eRegression Models\u003c/strong\u003e\u003cbr\u003e(\u003ca href=\"https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms\" rel=\"nofollow\"\u003eGlobalForecastingModel\u003c/a\u003e)\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html#darts.models.forecasting.regression_model.RegressionModel\" rel=\"nofollow\"\u003eRegressionModel\u003c/a\u003e: generic wrapper around any sklearn regression model\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅ ✅\u003c/td\u003e\n\u003ctd\u003e🔴 🔴\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.linear_regression_model.html#darts.models.forecasting.linear_regression_model.LinearRegressionModel\" rel=\"nofollow\"\u003eLinearRegressionModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.random_forest.html#darts.models.forecasting.random_forest.RandomForest\" rel=\"nofollow\"\u003eRandomForest\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅ ✅\u003c/td\u003e\n\u003ctd\u003e🔴 🔴\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel\" rel=\"nofollow\"\u003eLightGBMModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.xgboost.html#darts.models.forecasting.xgboost.XGBModel\" rel=\"nofollow\"\u003eXGBModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.catboost_model.html#darts.models.forecasting.catboost_model.CatBoostModel\" rel=\"nofollow\"\u003eCatBoostModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePyTorch (Lightning)-based Models\u003c/strong\u003e\u003cbr\u003e(\u003ca href=\"https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms\" rel=\"nofollow\"\u003eGlobalForecastingModel\u003c/a\u003e)\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel\" rel=\"nofollow\"\u003eRNNModel\u003c/a\u003e (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/abs/1704.04110\" rel=\"nofollow\"\u003eDeepAR paper\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e🔴 ✅ 🔴\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel\" rel=\"nofollow\"\u003eBlockRNNModel\u003c/a\u003e (incl. LSTM and GRU)\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html#darts.models.forecasting.nbeats.NBEATSModel\" rel=\"nofollow\"\u003eNBEATSModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/abs/1905.10437\" rel=\"nofollow\"\u003eN-BEATS paper\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nhits.html#darts.models.forecasting.nhits.NHiTSModel\" rel=\"nofollow\"\u003eNHiTSModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/abs/2201.12886\" rel=\"nofollow\"\u003eN-HiTS paper\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tcn_model.html#darts.models.forecasting.tcn_model.TCNModel\" rel=\"nofollow\"\u003eTCNModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/abs/1803.01271\" rel=\"nofollow\"\u003eTCN paper\u003c/a\u003e, \u003ca href=\"https://arxiv.org/abs/1906.04397\" rel=\"nofollow\"\u003eDeepTCN paper\u003c/a\u003e, \u003ca href=\"https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4\" rel=\"nofollow\"\u003eblog post\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.transformer_model.html#darts.models.forecasting.transformer_model.TransformerModel\" rel=\"nofollow\"\u003eTransformerModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ 🔴 🔴\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html#darts.models.forecasting.tft_model.TFTModel\" rel=\"nofollow\"\u003eTFTModel\u003c/a\u003e (Temporal Fusion Transformer)\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/pdf/1912.09363.pdf\" rel=\"nofollow\"\u003eTFT paper\u003c/a\u003e, \u003ca href=\"https://pytorch-forecasting.readthedocs.io/en/latest/models.html\" rel=\"nofollow\"\u003ePyTorch Forecasting\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel\" rel=\"nofollow\"\u003eDLinearModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/pdf/2205.13504.pdf\" rel=\"nofollow\"\u003eDLinear paper\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nlinear.html#darts.models.forecasting.nlinear.NLinearModel\" rel=\"nofollow\"\u003eNLinearModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/pdf/2205.13504.pdf\" rel=\"nofollow\"\u003eNLinear paper\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tide_model.html#darts.models.forecasting.tide_model.TiDEModel\" rel=\"nofollow\"\u003eTiDEModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/pdf/2304.08424.pdf\" rel=\"nofollow\"\u003eTiDE paper\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tsmixer_model.html#darts.models.forecasting.tsmixer_model.TSMixerModel\" rel=\"nofollow\"\u003eTSMixerModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/pdf/2303.06053.pdf\" rel=\"nofollow\"\u003eTSMixer paper\u003c/a\u003e, \u003ca href=\"https://github.com/ditschuk/pytorch-tsmixer\"\u003ePyTorch Implementation\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eEnsemble Models\u003c/strong\u003e\u003cbr\u003e(\u003ca href=\"https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms\" rel=\"nofollow\"\u003eGlobalForecastingModel\u003c/a\u003e): Model support is dependent on ensembled forecasting models and the ensemble model itself\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveEnsembleModel\" rel=\"nofollow\"\u003eNaiveEnsembleModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_ensemble_model.html#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel\" rel=\"nofollow\"\u003eRegressionEnsembleModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eConformal Models\u003c/strong\u003e\u003cbr\u003e(\u003ca href=\"https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms\" rel=\"nofollow\"\u003eGlobalForecastingModel\u003c/a\u003e): Model support is dependent on the forecasting model used\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.conformal_models.html#darts.models.forecasting.conformal_models.ConformalNaiveModel\" rel=\"nofollow\"\u003eConformalNaiveModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/pdf/1905.03222\" rel=\"nofollow\"\u003eConformalized Prediction\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://unit8co.github.io/darts/generated_api/darts.models.forecasting.conformal_models.html#darts.models.forecasting.conformal_models.ConformalQRModel\" rel=\"nofollow\"\u003eConformalQRModel\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://arxiv.org/pdf/1905.03222\" rel=\"nofollow\"\u003eConformalized Quantile Regression\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅ ✅\u003c/td\u003e\n\u003ctd\u003e✅\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCommunity \u0026amp; Contact\u003c/h2\u003e\u003ca id=\"user-content-community--contact\" class=\"anchor\" aria-label=\"Permalink: Community \u0026amp; Contact\" href=\"#community--contact\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAnyone is welcome to join our \u003ca href=\"https://gitter.im/u8darts/darts\" rel=\"nofollow\"\u003eGitter room\u003c/a\u003e to ask questions, make proposals,\ndiscuss use-cases, and more. If you spot a bug or have suggestions, GitHub issues are also welcome.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf what you want to tell us is not suitable for Gitter or Github,\nfeel free to send us an email at \u003ca href=\"mailto:darts@unit8.co\"\u003e\u003c/a\u003e\u003ca href=\"mailto:darts@unit8.co\"\u003edarts@unit8.co\u003c/a\u003e for\ndarts related matters or \u003ca href=\"mailto:info@unit8.co\"\u003e\u003c/a\u003e\u003ca href=\"mailto:info@unit8.co\"\u003einfo@unit8.co\u003c/a\u003e for any other\ninquiries.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContribute\u003c/h2\u003e\u003ca id=\"user-content-contribute\" class=\"anchor\" aria-label=\"Permalink: Contribute\" href=\"#contribute\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe development is ongoing, and we welcome suggestions, pull requests and issues on GitHub.\nAll contributors will be acknowledged on the\n\u003ca href=\"https://github.com/unit8co/darts/blob/master/CHANGELOG.md\"\u003echange log page\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eBefore working on a contribution (a new feature or a fix),\n\u003ca href=\"https://github.com/unit8co/darts/blob/master/CONTRIBUTING.md\"\u003echeck our contribution guidelines\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you are using Darts in your scientific work, we would appreciate citations to the following JMLR paper.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://www.jmlr.org/papers/v23/21-1177.html\" rel=\"nofollow\"\u003eDarts: User-Friendly Modern Machine Learning for Time Series\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eBibtex entry:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@article{JMLR:v23:21-1177,\n author = {Julien Herzen and Francesco Lässig and Samuele Giuliano Piazzetta and Thomas Neuer and Léo Tafti and Guillaume Raille and Tomas Van Pottelbergh and Marek Pasieka and Andrzej Skrodzki and Nicolas Huguenin and Maxime Dumonal and Jan KoÅ›cisz and Dennis Bader and Frédérick Gusset and Mounir Benheddi and Camila Williamson and Michal Kosinski and Matej Petrik and Gaël Grosch},\n title = {Darts: User-Friendly Modern Machine Learning for Time Series},\n journal = {Journal of Machine Learning Research},\n year = {2022},\n volume = {23},\n number = {124},\n pages = {1-6},\n url = {http://jmlr.org/papers/v23/21-1177.html}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@article{JMLR:v23:21-1177,\n author = {Julien Herzen and Francesco Lässig and Samuele Giuliano Piazzetta and Thomas Neuer and Léo Tafti and Guillaume Raille and Tomas Van Pottelbergh and Marek Pasieka and Andrzej Skrodzki and Nicolas Huguenin and Maxime Dumonal and Jan KoÅ›cisz and Dennis Bader and Frédérick Gusset and Mounir Benheddi and Camila Williamson and Michal Kosinski and Matej Petrik and Gaël Grosch},\n title = {Darts: User-Friendly Modern Machine Learning for Time Series},\n journal = {Journal of Machine Learning Research},\n year = {2022},\n volume = {23},\n number = {124},\n pages = {1-6},\n url = {http://jmlr.org/papers/v23/21-1177.html}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Time Series Made Easy in Python","anchor":"time-series-made-easy-in-python","htmlText":"Time Series Made Easy in Python"},{"level":2,"text":"Documentation","anchor":"documentation","htmlText":"Documentation"},{"level":5,"text":"High Level Introductions","anchor":"high-level-introductions","htmlText":"High Level Introductions"},{"level":5,"text":"Articles on Selected Topics","anchor":"articles-on-selected-topics","htmlText":"Articles on Selected Topics"},{"level":2,"text":"Quick Install","anchor":"quick-install","htmlText":"Quick Install"},{"level":2,"text":"Example Usage","anchor":"example-usage","htmlText":"Example Usage"},{"level":3,"text":"Forecasting","anchor":"forecasting","htmlText":"Forecasting"},{"level":3,"text":"Anomaly Detection","anchor":"anomaly-detection","htmlText":"Anomaly Detection"},{"level":2,"text":"Features","anchor":"features","htmlText":"Features"},{"level":2,"text":"Forecasting Models","anchor":"forecasting-models","htmlText":"Forecasting Models"},{"level":2,"text":"Community \u0026 Contact","anchor":"community--contact","htmlText":"Community \u0026amp; Contact"},{"level":2,"text":"Contribute","anchor":"contribute","htmlText":"Contribute"},{"level":2,"text":"Citation","anchor":"citation","htmlText":"Citation"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Funit8co%2Fdarts"}},{"displayName":"LICENSE","repoName":"darts","refName":"master","path":"LICENSE","preferredFileType":"license","tabName":"Apache-2.0","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Funit8co%2Fdarts"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-7d7eb7c71814.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-708ec8ade250.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":true,"react_blob_overlay":false,"accessible_code_button":true,"github_models_repo_integration":false}}}}</script> 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fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m11.28 3.22 4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.275-.326.749.749 0 0 1 .215-.734L13.94 8l-3.72-3.72a.749.749 0 0 1 .326-1.275.749.749 0 0 1 .734.215Zm-6.56 0a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042L2.06 8l3.72 3.72a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L.47 8.53a.75.75 0 0 1 0-1.06Z"></path></svg></span><span data-component="text" class="prc-Button-Label-pTQ3x">Code</span><span data-component="trailingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-triangle-down" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m4.427 7.427 3.396 3.396a.25.25 0 0 0 .354 0l3.396-3.396A.25.25 0 0 0 11.396 7H4.604a.25.25 0 0 0-.177.427Z"></path></svg></span></span></button><div 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data-hotkey="k"></button><h2 class="sr-only ScreenReaderHeading-module__userSelectNone--vW4Cq prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading" id="folders-and-files">Folders and files</h2><table aria-labelledby="folders-and-files" class="Box-sc-g0xbh4-0 fdROMU"><thead class="Box-sc-g0xbh4-0 jGKpsv"><tr class="Box-sc-g0xbh4-0 jdgHnn"><th colSpan="2" class="Box-sc-g0xbh4-0 bQivRW"><span class="text-bold">Name</span></th><th colSpan="1" class="Box-sc-g0xbh4-0 ldkMIO"><span class="text-bold">Name</span></th><th class="hide-sm"><div title="Last commit message" class="Truncate__StyledTruncate-sc-23o1d2-0 liVpTx width-fit"><span class="text-bold">Last commit message</span></div></th><th colSpan="1" class="Box-sc-g0xbh4-0 jMbWeI"><div title="Last commit date" class="Truncate__StyledTruncate-sc-23o1d2-0 liVpTx width-fit"><span class="text-bold">Last commit date</span></div></th></tr></thead><tbody><tr class="Box-sc-g0xbh4-0 gpqjiB"><td colSpan="3" class="bgColor-muted p-1 rounded-top-2"><div class="Box-sc-g0xbh4-0 dzCJzi"><h2 class="sr-only ScreenReaderHeading-module__userSelectNone--vW4Cq prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading">Latest commit</h2><div style="width:120px" class="Skeleton Skeleton--text" data-testid="loading"> </div><div class="d-flex flex-shrink-0 gap-2"><div data-testid="latest-commit-details" class="d-none d-sm-flex flex-items-center"></div><div class="d-flex gap-2"><h2 class="sr-only ScreenReaderHeading-module__userSelectNone--vW4Cq prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading">History</h2><a href="/unit8co/darts/commits/master/" class="prc-Button-ButtonBase-c50BI d-none d-lg-flex LinkButton-module__code-view-link-button--xvCGA flex-items-center fgColor-default" data-loading="false" data-size="small" data-variant="invisible" aria-describedby=":Raqj8pab:-loading-announcement"><span data-component="buttonContent" data-align="center" class="prc-Button-ButtonContent-HKbr-"><span data-component="leadingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-history" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m.427 1.927 1.215 1.215a8.002 8.002 0 1 1-1.6 5.685.75.75 0 1 1 1.493-.154 6.5 6.5 0 1 0 1.18-4.458l1.358 1.358A.25.25 0 0 1 3.896 6H.25A.25.25 0 0 1 0 5.75V2.104a.25.25 0 0 1 .427-.177ZM7.75 4a.75.75 0 0 1 .75.75v2.992l2.028.812a.75.75 0 0 1-.557 1.392l-2.5-1A.751.751 0 0 1 7 8.25v-3.5A.75.75 0 0 1 7.75 4Z"></path></svg></span><span data-component="text" class="prc-Button-Label-pTQ3x"><span class="fgColor-default">1,320 Commits</span></span></span></a><div class="d-sm-none"></div><div class="d-flex d-lg-none"><span role="tooltip" aria-label="1,320 Commits" id="history-icon-button-tooltip" class="Tooltip__TooltipBase-sc-17tf59c-0 hWlpPn tooltipped-n"><a 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colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="darts" aria-label="darts, (Directory)" class="Link--primary" href="/unit8co/darts/tree/master/darts">darts</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="darts" aria-label="darts, (Directory)" class="Link--primary" href="/unit8co/darts/tree/master/darts">darts</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-3"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="datasets" aria-label="datasets, (Directory)" class="Link--primary" href="/unit8co/darts/tree/master/datasets">datasets</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="datasets" aria-label="datasets, (Directory)" class="Link--primary" href="/unit8co/darts/tree/master/datasets">datasets</a></div></div></div></div></td><td class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-4"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="docs" aria-label="docs, (Directory)" class="Link--primary" href="/unit8co/darts/tree/master/docs">docs</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 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data-canonical-src="https://codecov.io/gh/unit8co/darts/branch/master/graph/badge.svg?token=7F1TLUFHQW" style="max-width: 100%;"></a> <a href="https://github.com/psf/black"><img src="https://camo.githubusercontent.com/5bf9e9fa18966df7cb5fac7715bef6b72df15e01a6efa9d616c83f9fcb527fe2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f64652532307374796c652d626c61636b2d3030303030302e737667" alt="Code style: black" data-canonical-src="https://img.shields.io/badge/code%20style-black-000000.svg" style="max-width: 100%;"></a> <a href="https://gitter.im/u8darts/darts?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge" rel="nofollow"><img src="https://camo.githubusercontent.com/45778b68a9cff427c6ca7e56ce613b0700ef6393b4aef417d8e4838e5629f7a4/68747470733a2f2f6261646765732e6769747465722e696d2f753864617274732f64617274732e737667" alt="Join the chat at https://gitter.im/u8darts/darts" data-canonical-src="https://badges.gitter.im/u8darts/darts.svg" style="max-width: 100%;"></a></p> <p dir="auto"><strong>Darts</strong> is a Python library for user-friendly forecasting and anomaly detection on time series. It contains a variety of models, from classics such as ARIMA to deep neural networks. The forecasting models can all be used in the same way, using <code>fit()</code> and <code>predict()</code> functions, similar to scikit-learn. The library also makes it easy to backtest models, combine the predictions of several models, and take external data into account. Darts supports both univariate and multivariate time series and models. The ML-based models can be trained on potentially large datasets containing multiple time series, and some of the models offer a rich support for probabilistic forecasting.</p> <p dir="auto">Darts also offers extensive anomaly detection capabilities. For instance, it is trivial to apply PyOD models on time series to obtain anomaly scores, or to wrap any of Darts forecasting or filtering models to obtain fully fledged anomaly detection models.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Documentation</h2><a id="user-content-documentation" class="anchor" aria-label="Permalink: Documentation" href="#documentation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://unit8co.github.io/darts/quickstart/00-quickstart.html" rel="nofollow">Quickstart</a></li> <li><a href="https://unit8co.github.io/darts/userguide.html" rel="nofollow">User Guide</a></li> <li><a href="https://unit8co.github.io/darts/generated_api/darts.html" rel="nofollow">API Reference</a></li> <li><a href="https://unit8co.github.io/darts/examples.html" rel="nofollow">Examples</a></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">High Level Introductions</h5><a id="user-content-high-level-introductions" class="anchor" aria-label="Permalink: High Level Introductions" href="#high-level-introductions"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://medium.com/unit8-machine-learning-publication/darts-time-series-made-easy-in-python-5ac2947a8878" rel="nofollow">Introductory Blog Post</a></li> <li><a href="https://youtu.be/g6OXDnXEtFA" rel="nofollow">Introduction video (25 minutes)</a></li> </ul> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">Articles on Selected Topics</h5><a id="user-content-articles-on-selected-topics" class="anchor" aria-label="Permalink: Articles on Selected Topics" href="#articles-on-selected-topics"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://medium.com/unit8-machine-learning-publication/training-forecasting-models-on-multiple-time-series-with-darts-dc4be70b1844" rel="nofollow">Training Models on Multiple Time Series</a></li> <li><a href="https://medium.com/unit8-machine-learning-publication/time-series-forecasting-using-past-and-future-external-data-with-darts-1f0539585993" rel="nofollow">Using Past and Future Covariates</a></li> <li><a href="https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4" rel="nofollow">Temporal Convolutional Networks and Forecasting</a></li> <li><a href="https://medium.com/unit8-machine-learning-publication/probabilistic-forecasting-in-darts-e88fbe83344e" rel="nofollow">Probabilistic Forecasting</a></li> <li><a href="https://medium.com/unit8-machine-learning-publication/transfer-learning-for-time-series-forecasting-87f39e375278" rel="nofollow">Transfer Learning for Time Series Forecasting</a></li> <li><a href="https://medium.com/unit8-machine-learning-publication/hierarchical-forecast-reconciliation-with-darts-8b4b058bb543" rel="nofollow">Hierarchical Forecast Reconciliation</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Quick Install</h2><a id="user-content-quick-install" class="anchor" aria-label="Permalink: Quick Install" href="#quick-install"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We recommend to first setup a clean Python environment for your project with Python 3.9+ using your favorite tool (<a href="https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html" title="conda-env" rel="nofollow">conda</a>, <a href="https://docs.python.org/3/library/venv.html" rel="nofollow">venv</a>, <a href="https://virtualenv.pypa.io/en/latest/" rel="nofollow">virtualenv</a> with or without <a href="https://virtualenvwrapper.readthedocs.io/en/latest/" rel="nofollow">virtualenvwrapper</a>).</p> <p dir="auto">Once your environment is set up you can install darts using pip:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="pip install darts"><pre class="notranslate"><code>pip install darts </code></pre></div> <p dir="auto">For more details you can refer to our <a href="https://github.com/unit8co/darts/blob/master/INSTALL.md">installation instructions</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Example Usage</h2><a id="user-content-example-usage" class="anchor" aria-label="Permalink: Example Usage" href="#example-usage"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Forecasting</h3><a id="user-content-forecasting" class="anchor" aria-label="Permalink: Forecasting" href="#forecasting"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Create a <code>TimeSeries</code> object from a Pandas DataFrame, and split it in train/validation series:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import pandas as pd from darts import TimeSeries # Read a pandas DataFrame df = pd.read_csv("AirPassengers.csv", delimiter=",") # Create a TimeSeries, specifying the time and value columns series = TimeSeries.from_dataframe(df, "Month", "#Passengers") # Set aside the last 36 months as a validation series train, val = series[:-36], series[-36:]"><pre><span class="pl-k">import</span> <span class="pl-s1">pandas</span> <span class="pl-k">as</span> <span class="pl-s1">pd</span> <span class="pl-k">from</span> <span class="pl-s1">darts</span> <span class="pl-k">import</span> <span class="pl-v">TimeSeries</span> <span class="pl-c"># Read a pandas DataFrame</span> <span class="pl-s1">df</span> <span class="pl-c1">=</span> <span class="pl-s1">pd</span>.<span class="pl-c1">read_csv</span>(<span class="pl-s">"AirPassengers.csv"</span>, <span class="pl-s1">delimiter</span><span class="pl-c1">=</span><span class="pl-s">","</span>) <span class="pl-c"># Create a TimeSeries, specifying the time and value columns</span> <span class="pl-s1">series</span> <span class="pl-c1">=</span> <span class="pl-v">TimeSeries</span>.<span class="pl-c1">from_dataframe</span>(<span class="pl-s1">df</span>, <span class="pl-s">"Month"</span>, <span class="pl-s">"#Passengers"</span>) <span class="pl-c"># Set aside the last 36 months as a validation series</span> <span class="pl-s1">train</span>, <span class="pl-s1">val</span> <span class="pl-c1">=</span> <span class="pl-s1">series</span>[:<span class="pl-c1">-</span><span class="pl-c1">36</span>], <span class="pl-s1">series</span>[<span class="pl-c1">-</span><span class="pl-c1">36</span>:]</pre></div> <p dir="auto">Fit an exponential smoothing model, and make a (probabilistic) prediction over the validation series' duration:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from darts.models import ExponentialSmoothing model = ExponentialSmoothing() model.fit(train) prediction = model.predict(len(val), num_samples=1000)"><pre><span class="pl-k">from</span> <span class="pl-s1">darts</span>.<span class="pl-s1">models</span> <span class="pl-k">import</span> <span class="pl-v">ExponentialSmoothing</span> <span class="pl-s1">model</span> <span class="pl-c1">=</span> <span class="pl-en">ExponentialSmoothing</span>() <span class="pl-s1">model</span>.<span class="pl-c1">fit</span>(<span class="pl-s1">train</span>) <span class="pl-s1">prediction</span> <span class="pl-c1">=</span> <span class="pl-s1">model</span>.<span class="pl-c1">predict</span>(<span class="pl-en">len</span>(<span class="pl-s1">val</span>), <span class="pl-s1">num_samples</span><span class="pl-c1">=</span><span class="pl-c1">1000</span>)</pre></div> <p dir="auto">Plot the median, 5th and 95th percentiles:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import matplotlib.pyplot as plt series.plot() prediction.plot(label="forecast", low_quantile=0.05, high_quantile=0.95) plt.legend()"><pre><span class="pl-k">import</span> <span class="pl-s1">matplotlib</span>.<span class="pl-s1">pyplot</span> <span class="pl-k">as</span> <span class="pl-s1">plt</span> <span class="pl-s1">series</span>.<span class="pl-c1">plot</span>() <span class="pl-s1">prediction</span>.<span class="pl-c1">plot</span>(<span class="pl-s1">label</span><span class="pl-c1">=</span><span class="pl-s">"forecast"</span>, <span class="pl-s1">low_quantile</span><span class="pl-c1">=</span><span class="pl-c1">0.05</span>, <span class="pl-s1">high_quantile</span><span class="pl-c1">=</span><span class="pl-c1">0.95</span>) <span class="pl-s1">plt</span>.<span class="pl-c1">legend</span>()</pre></div> <div dir="auto"> <a target="_blank" rel="noopener noreferrer" href="https://github.com/unit8co/darts/raw/master/static/images/example.png"><img src="https://github.com/unit8co/darts/raw/master/static/images/example.png" alt="darts forecast example" style="max-width: 100%;"></a> </div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Anomaly Detection</h3><a id="user-content-anomaly-detection" class="anchor" aria-label="Permalink: Anomaly Detection" href="#anomaly-detection"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Load a multivariate series, trim it, keep 2 components, split train and validation sets:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from darts.datasets import ETTh2Dataset series = ETTh2Dataset().load()[:10000][["MUFL", "LULL"]] train, val = series.split_before(0.6)"><pre><span class="pl-k">from</span> <span class="pl-s1">darts</span>.<span class="pl-s1">datasets</span> <span class="pl-k">import</span> <span class="pl-v">ETTh2Dataset</span> <span class="pl-s1">series</span> <span class="pl-c1">=</span> <span class="pl-en">ETTh2Dataset</span>().<span class="pl-c1">load</span>()[:<span class="pl-c1">10000</span>][[<span class="pl-s">"MUFL"</span>, <span class="pl-s">"LULL"</span>]] <span class="pl-s1">train</span>, <span class="pl-s1">val</span> <span class="pl-c1">=</span> <span class="pl-s1">series</span>.<span class="pl-c1">split_before</span>(<span class="pl-c1">0.6</span>)</pre></div> <p dir="auto">Build a k-means anomaly scorer, train it on the train set and use it on the validation set to get anomaly scores:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from darts.ad import KMeansScorer scorer = KMeansScorer(k=2, window=5) scorer.fit(train) anom_score = scorer.score(val)"><pre><span class="pl-k">from</span> <span class="pl-s1">darts</span>.<span class="pl-s1">ad</span> <span class="pl-k">import</span> <span class="pl-v">KMeansScorer</span> <span class="pl-s1">scorer</span> <span class="pl-c1">=</span> <span class="pl-en">KMeansScorer</span>(<span class="pl-s1">k</span><span class="pl-c1">=</span><span class="pl-c1">2</span>, <span class="pl-s1">window</span><span class="pl-c1">=</span><span class="pl-c1">5</span>) <span class="pl-s1">scorer</span>.<span class="pl-c1">fit</span>(<span class="pl-s1">train</span>) <span class="pl-s1">anom_score</span> <span class="pl-c1">=</span> <span class="pl-s1">scorer</span>.<span class="pl-c1">score</span>(<span class="pl-s1">val</span>)</pre></div> <p dir="auto">Build a binary anomaly detector and train it over train scores, then use it over validation scores to get binary anomaly classification:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from darts.ad import QuantileDetector detector = QuantileDetector(high_quantile=0.99) detector.fit(scorer.score(train)) binary_anom = detector.detect(anom_score)"><pre><span class="pl-k">from</span> <span class="pl-s1">darts</span>.<span class="pl-s1">ad</span> <span class="pl-k">import</span> <span class="pl-v">QuantileDetector</span> <span class="pl-s1">detector</span> <span class="pl-c1">=</span> <span class="pl-en">QuantileDetector</span>(<span class="pl-s1">high_quantile</span><span class="pl-c1">=</span><span class="pl-c1">0.99</span>) <span class="pl-s1">detector</span>.<span class="pl-c1">fit</span>(<span class="pl-s1">scorer</span>.<span class="pl-c1">score</span>(<span class="pl-s1">train</span>)) <span class="pl-s1">binary_anom</span> <span class="pl-c1">=</span> <span class="pl-s1">detector</span>.<span class="pl-c1">detect</span>(<span class="pl-s1">anom_score</span>)</pre></div> <p dir="auto">Plot (shifting and scaling some of the series to make everything appear on the same figure):</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import matplotlib.pyplot as plt series.plot() (anom_score / 2. - 100).plot(label="computed anomaly score", c="orangered", lw=3) (binary_anom * 45 - 150).plot(label="detected binary anomaly", lw=4)"><pre><span class="pl-k">import</span> <span class="pl-s1">matplotlib</span>.<span class="pl-s1">pyplot</span> <span class="pl-k">as</span> <span class="pl-s1">plt</span> <span class="pl-s1">series</span>.<span class="pl-c1">plot</span>() (<span class="pl-s1">anom_score</span> <span class="pl-c1">/</span> <span class="pl-c1">2.</span> <span class="pl-c1">-</span> <span class="pl-c1">100</span>).<span class="pl-c1">plot</span>(<span class="pl-s1">label</span><span class="pl-c1">=</span><span class="pl-s">"computed anomaly score"</span>, <span class="pl-s1">c</span><span class="pl-c1">=</span><span class="pl-s">"orangered"</span>, <span class="pl-s1">lw</span><span class="pl-c1">=</span><span class="pl-c1">3</span>) (<span class="pl-s1">binary_anom</span> <span class="pl-c1">*</span> <span class="pl-c1">45</span> <span class="pl-c1">-</span> <span class="pl-c1">150</span>).<span class="pl-c1">plot</span>(<span class="pl-s1">label</span><span class="pl-c1">=</span><span class="pl-s">"detected binary anomaly"</span>, <span class="pl-s1">lw</span><span class="pl-c1">=</span><span class="pl-c1">4</span>)</pre></div> <div dir="auto"> <a target="_blank" rel="noopener noreferrer" href="https://github.com/unit8co/darts/raw/master/static/images/example_ad.png"><img src="https://github.com/unit8co/darts/raw/master/static/images/example_ad.png" alt="darts anomaly detection example" style="max-width: 100%;"></a> </div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Features</h2><a id="user-content-features" class="anchor" aria-label="Permalink: Features" href="#features"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto"><strong>Forecasting Models:</strong> A large collection of forecasting models; from statistical models (such as ARIMA) to deep learning models (such as N-BEATS). See <a href="#forecasting-models">table of models below</a>.</p> </li> <li> <p dir="auto"><strong>Anomaly Detection</strong> The <code>darts.ad</code> module contains a collection of anomaly scorers, detectors and aggregators, which can all be combined to detect anomalies in time series. It is easy to wrap any of Darts forecasting or filtering models to build a fully fledged anomaly detection model that compares predictions with actuals. The <code>PyODScorer</code> makes it trivial to use PyOD detectors on time series.</p> </li> <li> <p dir="auto"><strong>Multivariate Support:</strong> <code>TimeSeries</code> can be multivariate - i.e., contain multiple time-varying dimensions/columns instead of a single scalar value. Many models can consume and produce multivariate series.</p> </li> <li> <p dir="auto"><strong>Multiple Series Training (Global Models):</strong> All machine learning based models (incl. all neural networks) support being trained on multiple (potentially multivariate) series. This can scale to large datasets too.</p> </li> <li> <p dir="auto"><strong>Probabilistic Support:</strong> <code>TimeSeries</code> objects can (optionally) represent stochastic time series; this can for instance be used to get confidence intervals, and many models support different flavours of probabilistic forecasting (such as estimating parametric distributions or quantiles). Some anomaly detection scorers are also able to exploit these predictive distributions.</p> </li> <li> <p dir="auto"><strong>Conformal Prediction Support:</strong> Our conformal prediction models allow to generate probabilistic forecasts with calibrated quantile intervals for any pre-trained global forecasting model.</p> </li> <li> <p dir="auto"><strong>Past and Future Covariates Support:</strong> Many models in Darts support past-observed and/or future-known covariate (external data) time series as inputs for producing forecasts.</p> </li> <li> <p dir="auto"><strong>Static Covariates Support:</strong> In addition to time-dependent data, <code>TimeSeries</code> can also contain static data for each dimension, which can be exploited by some models.</p> </li> <li> <p dir="auto"><strong>Hierarchical Reconciliation:</strong> Darts offers transformers to perform reconciliation. These can make the forecasts add up in a way that respects the underlying hierarchy.</p> </li> <li> <p dir="auto"><strong>Regression Models:</strong> It is possible to plug-in any scikit-learn compatible model to obtain forecasts as functions of lagged values of the target series and covariates.</p> </li> <li> <p dir="auto"><strong>Training with Sample Weights:</strong> All global models support being trained with sample weights. They can be applied to each observation, forecasted time step and target column.</p> </li> <li> <p dir="auto"><strong>Forecast Start Shifting:</strong> All global models support training and prediction on a shifted output window. This is useful for example for Day-Ahead Market forecasts, or when the covariates (or target series) are reported with a delay.</p> </li> <li> <p dir="auto"><strong>Explainability:</strong> Darts has the ability to <em>explain</em> some forecasting models using Shap values.</p> </li> <li> <p dir="auto"><strong>Data Processing:</strong> Tools to easily apply (and revert) common transformations on time series data (scaling, filling missing values, differencing, boxcox, ...)</p> </li> <li> <p dir="auto"><strong>Metrics:</strong> A variety of metrics for evaluating time series' goodness of fit; from R2-scores to Mean Absolute Scaled Error.</p> </li> <li> <p dir="auto"><strong>Backtesting:</strong> Utilities for simulating historical forecasts, using moving time windows.</p> </li> <li> <p dir="auto"><strong>PyTorch Lightning Support:</strong> All deep learning models are implemented using PyTorch Lightning, supporting among other things custom callbacks, GPUs/TPUs training and custom trainers.</p> </li> <li> <p dir="auto"><strong>Filtering Models:</strong> Darts offers three filtering models: <code>KalmanFilter</code>, <code>GaussianProcessFilter</code>, and <code>MovingAverageFilter</code>, which allow to filter time series, and in some cases obtain probabilistic inferences of the underlying states/values.</p> </li> <li> <p dir="auto"><strong>Datasets</strong> The <code>darts.datasets</code> submodule contains some popular time series datasets for rapid and reproducible experimentation.</p> </li> <li> <p dir="auto"><strong>Compatibility with Multiple Backends:</strong> <code>TimeSeries</code> objects can be created from and exported to various backends such as pandas, polars, numpy, pyarrow, xarray, and more, facilitating seamless integration with different data processing libraries.</p> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Forecasting Models</h2><a id="user-content-forecasting-models" class="anchor" aria-label="Permalink: Forecasting Models" href="#forecasting-models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Here's a breakdown of the forecasting models currently implemented in Darts. We are constantly working on bringing more models and features.</p> <markdown-accessiblity-table><table> <thead> <tr> <th>Model</th> <th>Sources</th> <th>Target Series Support:<br><br>Univariate/<br>Multivariate</th> <th>Covariates Support:<br><br>Past-observed/<br>Future-known/<br>Static</th> <th>Probabilistic Forecasting:<br><br>Sampled/<br>Distribution Parameters</th> <th>Training & Forecasting on Multiple Series</th> </tr> </thead> <tbody> <tr> <td><strong>Baseline Models</strong><br>(<a href="https://unit8co.github.io/darts/userguide/covariates.html#local-forecasting-models-lfms" rel="nofollow">LocalForecastingModel</a>)</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMean" rel="nofollow">NaiveMean</a></td> <td></td> <td>✅ ✅</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>🔴</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal" rel="nofollow">NaiveSeasonal</a></td> <td></td> <td>✅ ✅</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>🔴</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveDrift" rel="nofollow">NaiveDrift</a></td> <td></td> <td>✅ ✅</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>🔴</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMovingAverage" rel="nofollow">NaiveMovingAverage</a></td> <td></td> <td>✅ ✅</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>🔴</td> </tr> <tr> <td><strong>Statistical / Classic Models</strong><br>(<a href="https://unit8co.github.io/darts/userguide/covariates.html#local-forecasting-models-lfms" rel="nofollow">LocalForecastingModel</a>)</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.arima.html#darts.models.forecasting.arima.ARIMA" rel="nofollow">ARIMA</a></td> <td></td> <td>✅ 🔴</td> <td>🔴 ✅ 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.varima.html#darts.models.forecasting.varima.VARIMA" rel="nofollow">VARIMA</a></td> <td></td> <td>🔴 ✅</td> <td>🔴 ✅ 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_arima.html#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA" rel="nofollow">StatsForecastAutoArima</a> (faster AutoARIMA)</td> <td><a href="https://github.com/Nixtla/statsforecast">Nixtla's statsforecast</a></td> <td>✅ 🔴</td> <td>🔴 ✅ 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.exponential_smoothing.html#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing" rel="nofollow">ExponentialSmoothing</a></td> <td></td> <td>✅ 🔴</td> <td>🔴 🔴 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ets.html#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS" rel="nofollow">StatsforecastAutoETS</a></td> <td><a href="https://github.com/Nixtla/statsforecast">Nixtla's statsforecast</a></td> <td>✅ 🔴</td> <td>🔴 ✅ 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ces.html#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES" rel="nofollow">StatsforecastAutoCES</a></td> <td><a href="https://github.com/Nixtla/statsforecast">Nixtla's statsforecast</a></td> <td>✅ 🔴</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>🔴</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.BATS" rel="nofollow">BATS</a> and <a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.TBATS" rel="nofollow">TBATS</a></td> <td><a href="https://robjhyndman.com/papers/ComplexSeasonality.pdf" rel="nofollow">TBATS paper</a></td> <td>✅ 🔴</td> <td>🔴 🔴 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_tbats.html#darts.models.forecasting.sf_auto_tbats.StatsForecastAutoTBATS" rel="nofollow">StatsForecastAutoTBATS</a></td> <td><a href="https://github.com/Nixtla/statsforecast">Nixtla's statsforecast</a></td> <td>✅ 🔴</td> <td>🔴 🔴 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.Theta" rel="nofollow">Theta</a> and <a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.FourTheta" rel="nofollow">FourTheta</a></td> <td><a href="https://robjhyndman.com/papers/Theta.pdf" rel="nofollow">Theta</a> & <a href="https://github.com/Mcompetitions/M4-methods/blob/master/4Theta%20method.R">4 Theta</a></td> <td>✅ 🔴</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>🔴</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_theta.html#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta" rel="nofollow">StatsForecastAutoTheta</a></td> <td><a href="https://github.com/Nixtla/statsforecast">Nixtla's statsforecast</a></td> <td>✅ 🔴</td> <td>🔴 🔴 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet" rel="nofollow">Prophet</a></td> <td><a href="https://github.com/facebook/prophet">Prophet repo</a></td> <td>✅ 🔴</td> <td>🔴 ✅ 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.fft.html#darts.models.forecasting.fft.FFT" rel="nofollow">FFT</a> (Fast Fourier Transform)</td> <td></td> <td>✅ 🔴</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>🔴</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.kalman_forecaster.html#darts.models.forecasting.kalman_forecaster.KalmanForecaster" rel="nofollow">KalmanForecaster</a> using the Kalman filter and N4SID for system identification</td> <td><a href="https://people.duke.edu/~hpgavin/SystemID/References/VanOverschee-Automatica-1994.pdf" rel="nofollow">N4SID paper</a></td> <td>✅ ✅</td> <td>🔴 ✅ 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.croston.html#darts.models.forecasting.croston.Croston" rel="nofollow">Croston</a> method</td> <td></td> <td>✅ 🔴</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>🔴</td> </tr> <tr> <td><strong>Global Baseline Models</strong><br>(<a href="https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms" rel="nofollow">GlobalForecastingModel</a>)</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveAggregate" rel="nofollow">GlobalNaiveAggregate</a></td> <td></td> <td>✅ ✅</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveDrift" rel="nofollow">GlobalNaiveDrift</a></td> <td></td> <td>✅ ✅</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveSeasonal" rel="nofollow">GlobalNaiveSeasonal</a></td> <td></td> <td>✅ ✅</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>✅</td> </tr> <tr> <td><strong>Regression Models</strong><br>(<a href="https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms" rel="nofollow">GlobalForecastingModel</a>)</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html#darts.models.forecasting.regression_model.RegressionModel" rel="nofollow">RegressionModel</a>: generic wrapper around any sklearn regression model</td> <td></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>🔴 🔴</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.linear_regression_model.html#darts.models.forecasting.linear_regression_model.LinearRegressionModel" rel="nofollow">LinearRegressionModel</a></td> <td></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.random_forest.html#darts.models.forecasting.random_forest.RandomForest" rel="nofollow">RandomForest</a></td> <td></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>🔴 🔴</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel" rel="nofollow">LightGBMModel</a></td> <td></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.xgboost.html#darts.models.forecasting.xgboost.XGBModel" rel="nofollow">XGBModel</a></td> <td></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.catboost_model.html#darts.models.forecasting.catboost_model.CatBoostModel" rel="nofollow">CatBoostModel</a></td> <td></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><strong>PyTorch (Lightning)-based Models</strong><br>(<a href="https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms" rel="nofollow">GlobalForecastingModel</a>)</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel" rel="nofollow">RNNModel</a> (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version</td> <td><a href="https://arxiv.org/abs/1704.04110" rel="nofollow">DeepAR paper</a></td> <td>✅ ✅</td> <td>🔴 ✅ 🔴</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel" rel="nofollow">BlockRNNModel</a> (incl. LSTM and GRU)</td> <td></td> <td>✅ ✅</td> <td>✅ 🔴 🔴</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html#darts.models.forecasting.nbeats.NBEATSModel" rel="nofollow">NBEATSModel</a></td> <td><a href="https://arxiv.org/abs/1905.10437" rel="nofollow">N-BEATS paper</a></td> <td>✅ ✅</td> <td>✅ 🔴 🔴</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nhits.html#darts.models.forecasting.nhits.NHiTSModel" rel="nofollow">NHiTSModel</a></td> <td><a href="https://arxiv.org/abs/2201.12886" rel="nofollow">N-HiTS paper</a></td> <td>✅ ✅</td> <td>✅ 🔴 🔴</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tcn_model.html#darts.models.forecasting.tcn_model.TCNModel" rel="nofollow">TCNModel</a></td> <td><a href="https://arxiv.org/abs/1803.01271" rel="nofollow">TCN paper</a>, <a href="https://arxiv.org/abs/1906.04397" rel="nofollow">DeepTCN paper</a>, <a href="https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4" rel="nofollow">blog post</a></td> <td>✅ ✅</td> <td>✅ 🔴 🔴</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.transformer_model.html#darts.models.forecasting.transformer_model.TransformerModel" rel="nofollow">TransformerModel</a></td> <td></td> <td>✅ ✅</td> <td>✅ 🔴 🔴</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html#darts.models.forecasting.tft_model.TFTModel" rel="nofollow">TFTModel</a> (Temporal Fusion Transformer)</td> <td><a href="https://arxiv.org/pdf/1912.09363.pdf" rel="nofollow">TFT paper</a>, <a href="https://pytorch-forecasting.readthedocs.io/en/latest/models.html" rel="nofollow">PyTorch Forecasting</a></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel" rel="nofollow">DLinearModel</a></td> <td><a href="https://arxiv.org/pdf/2205.13504.pdf" rel="nofollow">DLinear paper</a></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nlinear.html#darts.models.forecasting.nlinear.NLinearModel" rel="nofollow">NLinearModel</a></td> <td><a href="https://arxiv.org/pdf/2205.13504.pdf" rel="nofollow">NLinear paper</a></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tide_model.html#darts.models.forecasting.tide_model.TiDEModel" rel="nofollow">TiDEModel</a></td> <td><a href="https://arxiv.org/pdf/2304.08424.pdf" rel="nofollow">TiDE paper</a></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tsmixer_model.html#darts.models.forecasting.tsmixer_model.TSMixerModel" rel="nofollow">TSMixerModel</a></td> <td><a href="https://arxiv.org/pdf/2303.06053.pdf" rel="nofollow">TSMixer paper</a>, <a href="https://github.com/ditschuk/pytorch-tsmixer">PyTorch Implementation</a></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><strong>Ensemble Models</strong><br>(<a href="https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms" rel="nofollow">GlobalForecastingModel</a>): Model support is dependent on ensembled forecasting models and the ensemble model itself</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveEnsembleModel" rel="nofollow">NaiveEnsembleModel</a></td> <td></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_ensemble_model.html#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel" rel="nofollow">RegressionEnsembleModel</a></td> <td></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><strong>Conformal Models</strong><br>(<a href="https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms" rel="nofollow">GlobalForecastingModel</a>): Model support is dependent on the forecasting model used</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.conformal_models.html#darts.models.forecasting.conformal_models.ConformalNaiveModel" rel="nofollow">ConformalNaiveModel</a></td> <td><a href="https://arxiv.org/pdf/1905.03222" rel="nofollow">Conformalized Prediction</a></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr> <td><a href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.conformal_models.html#darts.models.forecasting.conformal_models.ConformalQRModel" rel="nofollow">ConformalQRModel</a></td> <td><a href="https://arxiv.org/pdf/1905.03222" rel="nofollow">Conformalized Quantile Regression</a></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Community & Contact</h2><a id="user-content-community--contact" class="anchor" aria-label="Permalink: Community & Contact" href="#community--contact"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Anyone is welcome to join our <a href="https://gitter.im/u8darts/darts" rel="nofollow">Gitter room</a> to ask questions, make proposals, discuss use-cases, and more. If you spot a bug or have suggestions, GitHub issues are also welcome.</p> <p dir="auto">If what you want to tell us is not suitable for Gitter or Github, feel free to send us an email at <a href="mailto:darts@unit8.co"></a><a href="mailto:darts@unit8.co">darts@unit8.co</a> for darts related matters or <a href="mailto:info@unit8.co"></a><a href="mailto:info@unit8.co">info@unit8.co</a> for any other inquiries.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Contribute</h2><a id="user-content-contribute" class="anchor" aria-label="Permalink: Contribute" href="#contribute"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The development is ongoing, and we welcome suggestions, pull requests and issues on GitHub. All contributors will be acknowledged on the <a href="https://github.com/unit8co/darts/blob/master/CHANGELOG.md">change log page</a>.</p> <p dir="auto">Before working on a contribution (a new feature or a fix), <a href="https://github.com/unit8co/darts/blob/master/CONTRIBUTING.md">check our contribution guidelines</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citation</h2><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you are using Darts in your scientific work, we would appreciate citations to the following JMLR paper.</p> <p dir="auto"><a href="https://www.jmlr.org/papers/v23/21-1177.html" rel="nofollow">Darts: User-Friendly Modern Machine Learning for Time Series</a></p> <p dir="auto">Bibtex entry:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@article{JMLR:v23:21-1177, author = {Julien Herzen and Francesco Lässig and Samuele Giuliano Piazzetta and Thomas Neuer and Léo Tafti and Guillaume Raille and Tomas Van Pottelbergh and Marek Pasieka and Andrzej Skrodzki and Nicolas Huguenin and Maxime Dumonal and Jan KoÅ›cisz and Dennis Bader and Frédérick Gusset and Mounir Benheddi and Camila Williamson and Michal Kosinski and Matej Petrik and Gaël Grosch}, title = {Darts: User-Friendly Modern Machine Learning for Time Series}, journal = {Journal of Machine Learning Research}, year = {2022}, volume = {23}, number = {124}, pages = {1-6}, url = {http://jmlr.org/papers/v23/21-1177.html} }"><pre class="notranslate"><code>@article{JMLR:v23:21-1177, author = {Julien Herzen and Francesco Lässig and Samuele Giuliano Piazzetta and Thomas Neuer and Léo Tafti and Guillaume Raille and Tomas Van Pottelbergh and Marek Pasieka and Andrzej Skrodzki and Nicolas Huguenin and Maxime Dumonal and Jan KoÅ›cisz and Dennis Bader and Frédérick Gusset and Mounir Benheddi and Camila Williamson and Michal Kosinski and Matej Petrik and Gaël Grosch}, title = {Darts: User-Friendly Modern Machine Learning for Time Series}, journal = {Journal of Machine Learning Research}, year = {2022}, volume = {23}, number = {124}, pages = {1-6}, url = {http://jmlr.org/papers/v23/21-1177.html} } </code></pre></div> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="MWcCSi7ciB4a9qQ58zpPaMnWrs6Kipy0AWrkNfWC3YLYJI4/7/qtFk3xA/10IGekb7Lwzkb4eSoYUcgPugYLmQ==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> A python library for user-friendly forecasting and anomaly detection on time series. </p> <div class="my-3 d-flex flex-items-center"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link flex-shrink-0 mr-2"> <path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path> </svg> <span class="flex-auto min-width-0 css-truncate css-truncate-target width-fit"> <a title="https://unit8co.github.io/darts/" role="link" target="_blank" rel="noopener noreferrer nofollow" class="text-bold" href="https://unit8co.github.io/darts/">unit8co.github.io/darts/</a> </span> </div> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/python" title="Topic: python" data-view-component="true" class="topic-tag topic-tag-link"> python </a> <a href="/topics/data-science" title="Topic: data-science" data-view-component="true" class="topic-tag topic-tag-link"> data-science </a> <a href="/topics/machine-learning" title="Topic: machine-learning" data-view-component="true" class="topic-tag topic-tag-link"> machine-learning </a> <a href="/topics/deep-learning" title="Topic: deep-learning" data-view-component="true" class="topic-tag topic-tag-link"> deep-learning </a> <a href="/topics/time-series" title="Topic: time-series" data-view-component="true" class="topic-tag topic-tag-link"> time-series </a> <a href="/topics/forecasting" title="Topic: forecasting" data-view-component="true" class="topic-tag topic-tag-link"> forecasting </a> <a href="/topics/anomaly-detection" title="Topic: anomaly-detection" data-view-component="true" class="topic-tag topic-tag-link"> anomaly-detection </a> </div> </div> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" data-analytics-event="{"category":"Repository Overview","action":"click","label":"location:sidebar;file:readme"}" href="#readme-ov-file"> <svg 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text="gray" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-note mr-2"> <path d="M0 3.75C0 2.784.784 2 1.75 2h12.5c.966 0 1.75.784 1.75 1.75v8.5A1.75 1.75 0 0 1 14.25 14H1.75A1.75 1.75 0 0 1 0 12.25Zm1.75-.25a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25v-8.5a.25.25 0 0 0-.25-.25ZM3.5 6.25a.75.75 0 0 1 .75-.75h7a.75.75 0 0 1 0 1.5h-7a.75.75 0 0 1-.75-.75Zm.75 2.25h4a.75.75 0 0 1 0 1.5h-4a.75.75 0 0 1 0-1.5Z"></path> </svg> <span class="color-fg-muted">Custom properties</span></a> </div> <h3 class="sr-only">Stars</h3> <div class="mt-2"> <a href="/unit8co/darts/stargazers" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-star mr-2"> <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <strong>8.5k</strong> stars</a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/unit8co/darts/watchers" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-eye mr-2"> <path d="M8 2c1.981 0 3.671.992 4.933 2.078 1.27 1.091 2.187 2.345 2.637 3.023a1.62 1.62 0 0 1 0 1.798c-.45.678-1.367 1.932-2.637 3.023C11.67 13.008 9.981 14 8 14c-1.981 0-3.671-.992-4.933-2.078C1.797 10.83.88 9.576.43 8.898a1.62 1.62 0 0 1 0-1.798c.45-.677 1.367-1.931 2.637-3.022C4.33 2.992 6.019 2 8 2ZM1.679 7.932a.12.12 0 0 0 0 .136c.411.622 1.241 1.75 2.366 2.717C5.176 11.758 6.527 12.5 8 12.5c1.473 0 2.825-.742 3.955-1.715 1.124-.967 1.954-2.096 2.366-2.717a.12.12 0 0 0 0-.136c-.412-.621-1.242-1.75-2.366-2.717C10.824 4.242 9.473 3.5 8 3.5c-1.473 0-2.825.742-3.955 1.715-1.124.967-1.954 2.096-2.366 2.717ZM8 10a2 2 0 1 1-.001-3.999A2 2 0 0 1 8 10Z"></path> </svg> <strong>62</strong> watching</a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/unit8co/darts/forks" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-2"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <strong>921</strong> forks</a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Funit8co%2Fdarts&report=unit8co+%28user%29"> Report repository </a> </div> </div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame"> <a href="/unit8co/darts/releases" data-view-component="true" class="Link--primary no-underline Link">Releases <span title="47" data-view-component="true" class="Counter">47</span></a></h2> <a class="Link--primary d-flex no-underline" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/unit8co/darts/releases/tag/0.34.0"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag flex-shrink-0 mt-1 color-fg-success"> <path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <div class="ml-2 min-width-0"> <div class="d-flex"> <span class="css-truncate css-truncate-target text-bold mr-2" style="max-width: none;">Darts minor 0.34.0</span> <span title="Label: Latest" data-view-component="true" class="Label Label--success flex-shrink-0"> Latest </span> </div> <div class="text-small color-fg-muted"><relative-time datetime="2025-03-09T14:39:16Z" class="no-wrap">Mar 9, 2025</relative-time></div> </div> </a> <div data-view-component="true" class="mt-3"> <a text="small" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/unit8co/darts/releases" data-view-component="true" class="Link">+ 46 releases</a></div> </div> </div> <div class="BorderGrid-row" hidden> <div class="BorderGrid-cell"> <include-fragment src="/unit8co/darts/used_by_list" accept="text/fragment+html"> </include-fragment> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/unit8co/darts/graphs/contributors" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center">Contributors <span title="130" data-view-component="true" class="Counter ml-1">130</span></a></h2> <ul class="list-style-none d-flex flex-wrap mb-n2"> <li class="mb-2 mr-2" > <a href="https://github.com/hrzn" class="" data-hovercard-type="user" data-hovercard-url="/users/hrzn/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/4806678?s=64&v=4" alt="@hrzn" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/dennisbader" class="" data-hovercard-type="user" data-hovercard-url="/users/dennisbader/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/84317528?s=64&v=4" alt="@dennisbader" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/madtoinou" class="" data-hovercard-type="user" data-hovercard-url="/users/madtoinou/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/32447896?s=64&v=4" alt="@madtoinou" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/pennfranc" class="" data-hovercard-type="user" data-hovercard-url="/users/pennfranc/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/42946363?s=64&v=4" alt="@pennfranc" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/endrjuskr" class="" data-hovercard-type="user" data-hovercard-url="/users/endrjuskr/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/2611549?s=64&v=4" alt="@endrjuskr" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/LeoTafti" class="" data-hovercard-type="user" data-hovercard-url="/users/LeoTafti/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/43440848?s=64&v=4" alt="@LeoTafti" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/TheMP" class="" data-hovercard-type="user" data-hovercard-url="/users/TheMP/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/9071576?s=64&v=4" alt="@TheMP" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/tomasvanpottelbergh" class="" data-hovercard-type="user" data-hovercard-url="/users/tomasvanpottelbergh/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/85937396?s=64&v=4" alt="@tomasvanpottelbergh" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/MounBen" class="" data-hovercard-type="user" data-hovercard-url="/users/MounBen/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/41362408?s=64&v=4" alt="@MounBen" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/gdevos010" class="" data-hovercard-type="user" data-hovercard-url="/users/gdevos010/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/15316026?s=64&v=4" alt="@gdevos010" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/brunnedu" class="" data-hovercard-type="user" data-hovercard-url="/users/brunnedu/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/92083143?s=64&v=4" alt="@brunnedu" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/Droxef" class="" data-hovercard-type="user" data-hovercard-url="/users/Droxef/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/18333193?s=64&v=4" alt="@Droxef" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/piaz97" class="" data-hovercard-type="user" data-hovercard-url="/users/piaz97/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/25537171?s=64&v=4" alt="@piaz97" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/inverniz" class="" data-hovercard-type="user" data-hovercard-url="/users/inverniz/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/17392130?s=64&v=4" alt="@inverniz" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> </ul> <div data-view-component="true" class="mt-3"> <a text="small" href="/unit8co/darts/graphs/contributors" data-view-component="true" class="Link--inTextBlock Link">+ 116 contributors</a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3">Languages</h2> <div class="mb-2"> <span data-view-component="true" class="Progress"> <span style="background-color:#3572A5 !important;;width: 100.0%;" itemprop="keywords" data-view-component="true" class="Progress-item 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style="z-index: 999999" data-initial-cookie-consent-allowed="" data-cookie-consent-required="false"></ghcc-consent> <div id="ajax-error-message" class="ajax-error-message flash flash-error" hidden> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-alert"> <path d="M6.457 1.047c.659-1.234 2.427-1.234 3.086 0l6.082 11.378A1.75 1.75 0 0 1 14.082 15H1.918a1.75 1.75 0 0 1-1.543-2.575Zm1.763.707a.25.25 0 0 0-.44 0L1.698 13.132a.25.25 0 0 0 .22.368h12.164a.25.25 0 0 0 .22-.368Zm.53 3.996v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 11a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path> </svg> <button type="button" class="flash-close js-ajax-error-dismiss" aria-label="Dismiss error"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> You can’t perform that action at this time. </div> <template id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 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1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>