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class="reference internal nav-link" href="#documentation"> Documentation </a> <ul class="nav section-nav flex-column"> <li class="toc-h3 nav-item toc-entry"> <a class="reference internal nav-link" href="#high-level-introductions"> High Level Introductions </a> </li> <li class="toc-h3 nav-item toc-entry"> <a class="reference internal nav-link" href="#articles-on-selected-topics"> Articles on Selected Topics </a> </li> </ul> </li> <li class="toc-h2 nav-item toc-entry"> <a class="reference internal nav-link" href="#quick-install"> Quick Install </a> </li> <li class="toc-h2 nav-item toc-entry"> <a class="reference internal nav-link" href="#example-usage"> Example Usage </a> </li> <li class="toc-h2 nav-item toc-entry"> <a class="reference internal nav-link" href="#features"> Features </a> </li> <li class="toc-h2 nav-item toc-entry"> <a class="reference internal nav-link" href="#forecasting-models"> Forecasting Models </a> </li> <li class="toc-h2 nav-item toc-entry"> <a class="reference 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src="https://github.com/unit8co/darts/raw/master/static/images/darts-logo-trim.png" /></a> <hr class="docutils" /> <a class="reference external image-reference" href="https://badge.fury.io/py/darts"><img alt="PyPI version" src="https://badge.fury.io/py/u8darts.svg" /></a> <a class="reference external image-reference" href="https://anaconda.org/conda-forge/u8darts-all"><img alt="Conda Version" src="https://img.shields.io/conda/vn/conda-forge/u8darts-all.svg" /></a> <a class="reference external image-reference" href="https://img.shields.io/badge/python-3.9+-blue.svg"><img alt="Supported versions" src="https://img.shields.io/badge/python-3.9+-blue.svg" /></a> <a class="reference external image-reference" href="https://hub.docker.com/r/unit8/darts"><img alt="Docker Image Version (latest by date)" src="https://img.shields.io/docker/v/unit8/darts?label=docker&sort=date" /></a> <a class="reference external image-reference" href="https://img.shields.io/github/release-date/unit8co/darts"><img alt="GitHub Release Date" src="https://img.shields.io/github/release-date/unit8co/darts" /></a> <a class="reference external image-reference" href="https://img.shields.io/github/actions/workflow/status/unit8co/darts/release.yml?branch=master"><img alt="GitHub Workflow Status" src="https://img.shields.io/github/actions/workflow/status/unit8co/darts/release.yml?branch=master" /></a> <a class="reference external image-reference" href="https://pepy.tech/project/darts"><img alt="Downloads" src="https://pepy.tech/badge/darts" /></a> <a class="reference external image-reference" href="https://pepy.tech/project/u8darts"><img alt="Downloads" src="https://pepy.tech/badge/u8darts" /></a> <a class="reference external image-reference" href="https://codecov.io/gh/unit8co/darts"><img alt="codecov" src="https://codecov.io/gh/unit8co/darts/branch/master/graph/badge.svg?token=7F1TLUFHQW" /></a> <a class="reference external image-reference" href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg" /></a> <a class="reference external image-reference" href="https://gitter.im/u8darts/darts?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge"><img alt="Join the chat at https://gitter.im/u8darts/darts" src="https://badges.gitter.im/u8darts/darts.svg" /></a> Darts 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 class="docutils literal notranslate">fit()</code> and <code class="docutils literal notranslate">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. 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. <section id="documentation"> <h2>Documentation<a class="headerlink" href="#documentation" title="Permalink to this heading">¶</a></h2> <ul class="simple"> <li><a class="reference external" href="https://unit8co.github.io/darts/quickstart/00-quickstart.html">Quickstart</a></li> <li><a class="reference external" href="https://unit8co.github.io/darts/userguide.html">User Guide</a></li> <li><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.html">API Reference</a></li> <li><a class="reference external" href="https://unit8co.github.io/darts/examples.html">Examples</a></li> </ul> <section id="high-level-introductions"> <h3>High Level Introductions<a class="headerlink" href="#high-level-introductions" title="Permalink to this heading">¶</a></h3> <ul class="simple"> <li><a class="reference external" href="https://medium.com/unit8-machine-learning-publication/darts-time-series-made-easy-in-python-5ac2947a8878">Introductory Blog Post</a></li> <li><a class="reference external" href="https://youtu.be/g6OXDnXEtFA">Introduction video (25 minutes)</a></li> </ul> </section> <section id="articles-on-selected-topics"> <h3>Articles on Selected Topics<a class="headerlink" href="#articles-on-selected-topics" title="Permalink to this heading">¶</a></h3> <ul class="simple"> <li><a class="reference external" href="https://medium.com/unit8-machine-learning-publication/training-forecasting-models-on-multiple-time-series-with-darts-dc4be70b1844">Training Models on Multiple Time Series</a></li> <li><a class="reference external" href="https://medium.com/unit8-machine-learning-publication/time-series-forecasting-using-past-and-future-external-data-with-darts-1f0539585993">Using Past and Future Covariates</a></li> <li><a class="reference external" href="https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4">Temporal Convolutional Networks and Forecasting</a></li> <li><a class="reference external" href="https://medium.com/unit8-machine-learning-publication/probabilistic-forecasting-in-darts-e88fbe83344e">Probabilistic Forecasting</a></li> <li><a class="reference external" href="https://medium.com/unit8-machine-learning-publication/transfer-learning-for-time-series-forecasting-87f39e375278">Transfer Learning for Time Series Forecasting</a></li> <li><a class="reference external" href="https://medium.com/unit8-machine-learning-publication/hierarchical-forecast-reconciliation-with-darts-8b4b058bb543">Hierarchical Forecast Reconciliation</a></li> </ul> </section> </section> <section id="quick-install"> <h2>Quick Install<a class="headerlink" href="#quick-install" title="Permalink to this heading">¶</a></h2> 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">conda</a>, <a class="reference external" href="https://docs.python.org/3/library/venv.html">venv</a>, <a class="reference external" href="https://virtualenv.pypa.io/en/latest/">virtualenv</a> with or without <a class="reference external" href="https://virtualenvwrapper.readthedocs.io/en/latest/">virtualenvwrapper</a>). Once your environment is set up you can install darts using pip: <div class="highlight-default notranslate"><div class="highlight"><pre>pip install darts </pre></div> </div> For more details you can refer to our <a class="reference external" href="https://github.com/unit8co/darts/blob/master/INSTALL.md">installation instructions</a>. </section> <section id="example-usage"> <h2>Example Usage<a class="headerlink" href="#example-usage" title="Permalink to this heading">¶</a></h2> Create a <code class="docutils literal notranslate">TimeSeries</code> object from a Pandas DataFrame, and split it in train/validation series: <div class="highlight-python notranslate"><div class="highlight"><pre>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></div> </div> Fit an exponential smoothing model, and make a (probabilistic) prediction over the validation series’ duration: <div class="highlight-python notranslate"><div class="highlight"><pre>from darts.models import ExponentialSmoothing model = ExponentialSmoothing() model.fit(train) prediction = model.predict(len(val), num_samples=1000) </pre></div> </div> Plot the median, 5th and 95th percentiles: <div class="highlight-python notranslate"><div class="highlight"><pre>import matplotlib.pyplot as plt series.plot() prediction.plot(label="forecast", low_quantile=0.05, high_quantile=0.95) plt.legend() </pre></div> </div> <div style="text-align:center;"> <img src="https://github.com/unit8co/darts/raw/master/static/images/example.png" alt="darts forecast example" /> </div>Load a multivariate series, trim it, keep 2 components, split train and validation sets: <div class="highlight-python notranslate"><div class="highlight"><pre>from darts.datasets import ETTh2Dataset series = ETTh2Dataset().load()[:10000][["MUFL", "LULL"]] train, val = series.split_before(0.6) </pre></div> </div> Build a k-means anomaly scorer, train it on the train set and use it on the validation set to get anomaly scores: <div class="highlight-python notranslate"><div class="highlight"><pre>from darts.ad import KMeansScorer scorer = KMeansScorer(k=2, window=5) scorer.fit(train) anom_score = scorer.score(val) </pre></div> </div> Build a binary anomaly detector and train it over train scores, then use it over validation scores to get binary anomaly classification: <div class="highlight-python notranslate"><div class="highlight"><pre>from darts.ad import QuantileDetector detector = QuantileDetector(high_quantile=0.99) detector.fit(scorer.score(train)) binary_anom = detector.detect(anom_score) </pre></div> </div> Plot (shifting and scaling some of the series to make everything appear on the same figure): <div class="highlight-python notranslate"><div class="highlight"><pre>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></div> </div> <div style="text-align:center;"> <img src="https://github.com/unit8co/darts/raw/master/static/images/example_ad.png" alt="darts anomaly detection example" /> </div></section> <section id="features"> <h2>Features<a class="headerlink" href="#features" title="Permalink to this heading">¶</a></h2> <ul class="simple"> <li>Forecasting Models: A large collection of forecasting models; from statistical models (such as ARIMA) to deep learning models (such as N-BEATS). See <a class="reference external" href="#forecasting-models">table of models below</a>.</li> <li>Anomaly Detection The <code class="docutils literal notranslate">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 class="docutils literal notranslate">PyODScorer</code> makes it trivial to use PyOD detectors on time series.</li> <li>Multivariate Support: <code class="docutils literal notranslate">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.</li> <li>Multiple Series Training (Global Models): All machine learning based models (incl. all neural networks) support being trained on multiple (potentially multivariate) series. This can scale to large datasets too.</li> <li>Probabilistic Support: <code class="docutils literal notranslate">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.</li> <li>Conformal Prediction Support: Our conformal prediction models allow to generate probabilistic forecasts with calibrated quantile intervals for any pre-trained global forecasting model.</li> <li>Past and Future Covariates Support: Many models in Darts support past-observed and/or future-known covariate (external data) time series as inputs for producing forecasts.</li> <li>Static Covariates Support: In addition to time-dependent data, <code class="docutils literal notranslate">TimeSeries</code> can also contain static data for each dimension, which can be exploited by some models.</li> <li>Hierarchical Reconciliation: Darts offers transformers to perform reconciliation. These can make the forecasts add up in a way that respects the underlying hierarchy.</li> <li>Regression Models: 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.</li> <li>Training with Sample Weights: All global models support being trained with sample weights. They can be applied to each observation, forecasted time step and target column.</li> <li>Forecast Start Shifting: 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.</li> <li>Explainability: Darts has the ability to explain some forecasting models using Shap values.</li> <li>Data Processing: Tools to easily apply (and revert) common transformations on time series data (scaling, filling missing values, differencing, boxcox, …)</li> <li>Metrics: A variety of metrics for evaluating time series’ goodness of fit; from R2-scores to Mean Absolute Scaled Error.</li> <li>Backtesting: Utilities for simulating historical forecasts, using moving time windows.</li> <li>PyTorch Lightning Support: All deep learning models are implemented using PyTorch Lightning, supporting among other things custom callbacks, GPUs/TPUs training and custom trainers.</li> <li>Filtering Models: Darts offers three filtering models: <code class="docutils literal notranslate">KalmanFilter</code>, <code class="docutils literal notranslate">GaussianProcessFilter</code>, and <code class="docutils literal notranslate">MovingAverageFilter</code>, which allow to filter time series, and in some cases obtain probabilistic inferences of the underlying states/values.</li> <li>Datasets The <code class="docutils literal notranslate">darts.datasets</code> submodule contains some popular time series datasets for rapid and reproducible experimentation.</li> <li>Compatibility with Multiple Backends: <code class="docutils literal notranslate">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.</li> </ul> </section> <section id="forecasting-models"> <h2>Forecasting Models<a class="headerlink" href="#forecasting-models" title="Permalink to this heading">¶</a></h2> Here’s a breakdown of the forecasting models currently implemented in Darts. We are constantly working on bringing more models and features. <table class="table"> <colgroup> <col style="width: 17%" /> <col style="width: 17%" /> <col style="width: 17%" /> <col style="width: 17%" /> <col style="width: 17%" /> <col style="width: 17%" /> </colgroup> <thead> <tr class="row-odd"><th class="head">Model</th> <th class="head">Sources</th> <th class="head">Target Series Support: Univariate/ Multivariate</th> <th class="head">Covariates Support: Past-observed/ Future-known/ Static</th> <th class="head">Probabilistic Forecasting: Sampled/ Distribution Parameters</th> <th class="head">Training & Forecasting on Multiple Series</th> </tr> </thead> <tbody> <tr class="row-even"><td>Baseline Models (<a class="reference external" href="https://unit8co.github.io/darts/userguide/covariates.html#local-forecasting-models-lfms">LocalForecastingModel</a>)</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMean">NaiveMean</a></td> <td></td> <td>✅ ✅</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>🔴</td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal">NaiveSeasonal</a></td> <td></td> <td>✅ ✅</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>🔴</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveDrift">NaiveDrift</a></td> <td></td> <td>✅ ✅</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>🔴</td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMovingAverage">NaiveMovingAverage</a></td> <td></td> <td>✅ ✅</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>🔴</td> </tr> <tr class="row-odd"><td>Statistical / Classic Models (<a class="reference external" href="https://unit8co.github.io/darts/userguide/covariates.html#local-forecasting-models-lfms">LocalForecastingModel</a>)</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.arima.html#darts.models.forecasting.arima.ARIMA">ARIMA</a></td> <td></td> <td>✅ 🔴</td> <td>🔴 ✅ 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.varima.html#darts.models.forecasting.varima.VARIMA">VARIMA</a></td> <td></td> <td>🔴 ✅</td> <td>🔴 ✅ 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.auto_arima.html#darts.models.forecasting.auto_arima.AutoARIMA">AutoARIMA</a></td> <td></td> <td>✅ 🔴</td> <td>🔴 ✅ 🔴</td> <td>🔴 🔴</td> <td>🔴</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_arima.html#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA">StatsForecastAutoArima</a> (faster AutoARIMA)</td> <td><a class="reference external" href="https://github.com/Nixtla/statsforecast">Nixtla’s statsforecast</a></td> <td>✅ 🔴</td> <td>🔴 ✅ 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.exponential_smoothing.html#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing">ExponentialSmoothing</a></td> <td></td> <td>✅ 🔴</td> <td>🔴 🔴 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ets.html#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS">StatsforecastAutoETS</a></td> <td><a class="reference external" href="https://github.com/Nixtla/statsforecast">Nixtla’s statsforecast</a></td> <td>✅ 🔴</td> <td>🔴 ✅ 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ces.html#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES">StatsforecastAutoCES</a></td> <td><a class="reference external" href="https://github.com/Nixtla/statsforecast">Nixtla’s statsforecast</a></td> <td>✅ 🔴</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>🔴</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.BATS">BATS</a> and <a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.TBATS">TBATS</a></td> <td><a class="reference external" href="https://robjhyndman.com/papers/ComplexSeasonality.pdf">TBATS paper</a></td> <td>✅ 🔴</td> <td>🔴 🔴 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_tbats.html#darts.models.forecasting.sf_auto_tbats.StatsForecastAutoTBATS">StatsForecastAutoTBATS</a></td> <td><a class="reference external" href="https://github.com/Nixtla/statsforecast">Nixtla’s statsforecast</a></td> <td>✅ 🔴</td> <td>🔴 🔴 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.Theta">Theta</a> and <a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.FourTheta">FourTheta</a></td> <td><a class="reference external" href="https://robjhyndman.com/papers/Theta.pdf">Theta</a> & <a class="reference external" 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 class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_theta.html#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta">StatsForecastAutoTheta</a></td> <td><a class="reference external" href="https://github.com/Nixtla/statsforecast">Nixtla’s statsforecast</a></td> <td>✅ 🔴</td> <td>🔴 🔴 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet">Prophet</a></td> <td><a class="reference external" href="https://github.com/facebook/prophet">Prophet repo</a></td> <td>✅ 🔴</td> <td>🔴 ✅ 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.fft.html#darts.models.forecasting.fft.FFT">FFT</a> (Fast Fourier Transform)</td> <td></td> <td>✅ 🔴</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>🔴</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.kalman_forecaster.html#darts.models.forecasting.kalman_forecaster.KalmanForecaster">KalmanForecaster</a> using the Kalman filter and N4SID for system identification</td> <td><a class="reference external" href="https://people.duke.edu/~hpgavin/SystemID/References/VanOverschee-Automatica-1994.pdf">N4SID paper</a></td> <td>✅ ✅</td> <td>🔴 ✅ 🔴</td> <td>✅ 🔴</td> <td>🔴</td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.croston.html#darts.models.forecasting.croston.Croston">Croston</a> method</td> <td></td> <td>✅ 🔴</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>🔴</td> </tr> <tr class="row-odd"><td>Global Baseline Models (<a class="reference external" href="https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms">GlobalForecastingModel</a>)</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveAggregate">GlobalNaiveAggregate</a></td> <td></td> <td>✅ ✅</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>✅</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveDrift">GlobalNaiveDrift</a></td> <td></td> <td>✅ ✅</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>✅</td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveSeasonal">GlobalNaiveSeasonal</a></td> <td></td> <td>✅ ✅</td> <td>🔴 🔴 🔴</td> <td>🔴 🔴</td> <td>✅</td> </tr> <tr class="row-odd"><td>Regression Models (<a class="reference external" href="https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms">GlobalForecastingModel</a>)</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html#darts.models.forecasting.regression_model.RegressionModel">RegressionModel</a>: generic wrapper around any sklearn regression model</td> <td></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>🔴 🔴</td> <td>✅</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.linear_regression_model.html#darts.models.forecasting.linear_regression_model.LinearRegressionModel">LinearRegressionModel</a></td> <td></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.random_forest.html#darts.models.forecasting.random_forest.RandomForest">RandomForest</a></td> <td></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>🔴 🔴</td> <td>✅</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel">LightGBMModel</a></td> <td></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.xgboost.html#darts.models.forecasting.xgboost.XGBModel">XGBModel</a></td> <td></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.catboost_model.html#darts.models.forecasting.catboost_model.CatBoostModel">CatBoostModel</a></td> <td></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-even"><td>PyTorch (Lightning)-based Models (<a class="reference external" href="https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms">GlobalForecastingModel</a>)</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel">RNNModel</a> (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version</td> <td><a class="reference external" href="https://arxiv.org/abs/1704.04110">DeepAR paper</a></td> <td>✅ ✅</td> <td>🔴 ✅ 🔴</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel">BlockRNNModel</a> (incl. LSTM and GRU)</td> <td></td> <td>✅ ✅</td> <td>✅ 🔴 🔴</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html#darts.models.forecasting.nbeats.NBEATSModel">NBEATSModel</a></td> <td><a class="reference external" href="https://arxiv.org/abs/1905.10437">N-BEATS paper</a></td> <td>✅ ✅</td> <td>✅ 🔴 🔴</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nhits.html#darts.models.forecasting.nhits.NHiTSModel">NHiTSModel</a></td> <td><a class="reference external" href="https://arxiv.org/abs/2201.12886">N-HiTS paper</a></td> <td>✅ ✅</td> <td>✅ 🔴 🔴</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tcn_model.html#darts.models.forecasting.tcn_model.TCNModel">TCNModel</a></td> <td><a class="reference external" href="https://arxiv.org/abs/1803.01271">TCN paper</a>, <a class="reference external" href="https://arxiv.org/abs/1906.04397">DeepTCN paper</a>, <a class="reference external" href="https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4">blog post</a></td> <td>✅ ✅</td> <td>✅ 🔴 🔴</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.transformer_model.html#darts.models.forecasting.transformer_model.TransformerModel">TransformerModel</a></td> <td></td> <td>✅ ✅</td> <td>✅ 🔴 🔴</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html#darts.models.forecasting.tft_model.TFTModel">TFTModel</a> (Temporal Fusion Transformer)</td> <td><a class="reference external" href="https://arxiv.org/pdf/1912.09363.pdf">TFT paper</a>, <a class="reference external" href="https://pytorch-forecasting.readthedocs.io/en/latest/models.html">PyTorch Forecasting</a></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel">DLinearModel</a></td> <td><a class="reference external" href="https://arxiv.org/pdf/2205.13504.pdf">DLinear paper</a></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nlinear.html#darts.models.forecasting.nlinear.NLinearModel">NLinearModel</a></td> <td><a class="reference external" href="https://arxiv.org/pdf/2205.13504.pdf">NLinear paper</a></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tide_model.html#darts.models.forecasting.tide_model.TiDEModel">TiDEModel</a></td> <td><a class="reference external" href="https://arxiv.org/pdf/2304.08424.pdf">TiDE paper</a></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tsmixer_model.html#darts.models.forecasting.tsmixer_model.TSMixerModel">TSMixerModel</a></td> <td><a class="reference external" href="https://arxiv.org/pdf/2303.06053.pdf">TSMixer paper</a>, <a class="reference external" href="https://github.com/ditschuk/pytorch-tsmixer">PyTorch Implementation</a></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-even"><td>Ensemble Models (<a class="reference external" href="https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms">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 class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveEnsembleModel">NaiveEnsembleModel</a></td> <td></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_ensemble_model.html#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel">RegressionEnsembleModel</a></td> <td></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-odd"><td>Conformal Models (<a class="reference external" href="https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms">GlobalForecastingModel</a>): Model support is dependent on the forecasting model used</td> <td></td> <td></td> <td></td> <td></td> <td></td> </tr> <tr class="row-even"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.conformal_models.html#darts.models.forecasting.conformal_models.ConformalNaiveModel">ConformalNaiveModel</a></td> <td><a class="reference external" href="https://arxiv.org/pdf/1905.03222">Conformalized Prediction</a></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> <tr class="row-odd"><td><a class="reference external" href="https://unit8co.github.io/darts/generated_api/darts.models.forecasting.conformal_models.html#darts.models.forecasting.conformal_models.ConformalQRModel">ConformalQRModel</a></td> <td><a class="reference external" href="https://arxiv.org/pdf/1905.03222">Conformalized Quantile Regression</a></td> <td>✅ ✅</td> <td>✅ ✅ ✅</td> <td>✅ ✅</td> <td>✅</td> </tr> </tbody> </table> </section> <section id="community-contact"> <h2>Community & Contact<a class="headerlink" href="#community-contact" title="Permalink to this heading">¶</a></h2> Anyone is welcome to join our <a class="reference external" href="https://gitter.im/u8darts/darts">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. 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">darts@unit8.co</a> for darts related matters or <a href="mailto:info@unit8.co">info@unit8.co</a> for any other inquiries. </section> <section id="contribute"> <h2>Contribute<a class="headerlink" href="#contribute" title="Permalink to this heading">¶</a></h2> The development is ongoing, and we welcome suggestions, pull requests and issues on GitHub. All contributors will be acknowledged on the <a class="reference external" href="https://github.com/unit8co/darts/blob/master/CHANGELOG.md">change log page</a>. Before working on a contribution (a new feature or a fix), <a class="reference external" href="https://github.com/unit8co/darts/blob/master/CONTRIBUTING.md">check our contribution guidelines</a>. </section> <section id="citation"> <h2>Citation<a class="headerlink" href="#citation" title="Permalink to this heading">¶</a></h2> If you are using Darts in your scientific work, we would appreciate citations to the following JMLR paper. <a class="reference external" href="https://www.jmlr.org/papers/v23/21-1177.html">Darts: User-Friendly Modern Machine Learning for Time Series</a> Bibtex entry: <div class="highlight-default notranslate"><div class="highlight"><pre>@article{JMLR:v23:21-1177, author = {Julien Herzen and Francesco LÃ¤ssig and Samuele Giuliano Piazzetta and Thomas Neuer and LÃ©o 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 FrÃ©dÃ©rick Gusset and Mounir Benheddi and Camila Williamson and Michal Kosinski and Matej Petrik and GaÃ«l 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></div> </div> <div class="toctree-wrapper compound"> </div> <div class="toctree-wrapper compound"> </div> <div class="toctree-wrapper compound"> </div> <div class="toctree-wrapper compound"> </div> <div class="toctree-wrapper compound"> </div> <div class="toctree-wrapper compound"> </div> </section> </section> <section id="indices-and-tables"> <h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this heading">¶</a></h1> <ul class="simple"> <li><a class="reference internal" href="genindex.html">Index</a></li> <li><a class="reference internal" href="py-modindex.html">Module Index</a></li> <li><a class="reference internal" href="search.html">Search Page</a></li> </ul> </section> </div>  <div class='prev-next-area'> <a class='right-next' id="next-link" href="README.html" title="next page"> <div class="prev-next-info"> next Time Series Made Easy in Python </div> </a> </div> </main> </div> </div> <script src="_static/js/index.be7d3bbb2ef33a8344ce.js"></script> <footer class="footer mt-5 mt-md-0"> <div class="container"> <div class="footer-item"> © Copyright 2020 - 2025, Unit8 SA (Apache 2.0 License). </div> <div class="footer-item"> Created using <a href="http://sphinx-doc.org/">Sphinx</a> 5.0.0. </div> </div> </footer> </body> </html>