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class="menu-text">Inference</span></a> </div> </li> <li class="sidebar-item"> <div class="sidebar-item-container"> <a href="./analysis.html" class="sidebar-item-text sidebar-link"> <span class="menu-text">Analysis</span></a> </div> </li> <li class="sidebar-item"> <div class="sidebar-item-container"> <a href="./models.explainability.html" class="sidebar-item-text sidebar-link"> <span class="menu-text">Explainability</span></a> </div> </li> <li class="sidebar-item"> <div class="sidebar-item-container"> <a href="./utils.html" class="sidebar-item-text sidebar-link"> <span class="menu-text">Utilities</span></a> </div> </li> </ul> </div> </nav> <div id="quarto-sidebar-glass" class="quarto-sidebar-collapse-item" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item"></div>  <div id="quarto-margin-sidebar" class="sidebar margin-sidebar"> <nav id="TOC" role="doc-toc" class="toc-active"> <h2 id="toc-title">On this page</h2> <ul> <li><a href="#description" id="toc-description" class="nav-link active" data-scroll-target="#description">Description</a></li> <li><a href="#whats-new" id="toc-whats-new" class="nav-link" data-scroll-target="#whats-new">What’s new:</a></li> <li><a href="#installation" id="toc-installation" class="nav-link" data-scroll-target="#installation">Installation</a> <ul> <li><a href="#pip-install" id="toc-pip-install" class="nav-link" data-scroll-target="#pip-install">Pip install</a></li> <li><a href="#conda-install" id="toc-conda-install" class="nav-link" data-scroll-target="#conda-install">Conda install</a></li> </ul></li> <li><a href="#documentation" id="toc-documentation" class="nav-link" data-scroll-target="#documentation">Documentation</a></li> <li><a href="#available-models" id="toc-available-models" class="nav-link" data-scroll-target="#available-models">Available models:</a></li> <li><a href="#how-to-start-using-tsai" id="toc-how-to-start-using-tsai" class="nav-link" data-scroll-target="#how-to-start-using-tsai">How to start using tsai?</a></li> <li><a href="#examples" id="toc-examples" class="nav-link" data-scroll-target="#examples">Examples</a> <ul> <li><a href="#binary-univariate-classification" id="toc-binary-univariate-classification" class="nav-link" data-scroll-target="#binary-univariate-classification">Binary, univariate classification</a></li> <li><a href="#multi-class-multivariate-classification" id="toc-multi-class-multivariate-classification" class="nav-link" data-scroll-target="#multi-class-multivariate-classification">Multi-class, multivariate classification</a></li> <li><a href="#multivariate-regression" id="toc-multivariate-regression" class="nav-link" data-scroll-target="#multivariate-regression">Multivariate Regression</a></li> <li><a href="#forecasting" id="toc-forecasting" class="nav-link" data-scroll-target="#forecasting">Forecasting</a> <ul class="collapse"> <li><a href="#single-step" id="toc-single-step" class="nav-link" data-scroll-target="#single-step">Single step</a></li> <li><a href="#multi-step" id="toc-multi-step" class="nav-link" data-scroll-target="#multi-step">Multi-step</a></li> </ul></li> </ul></li> <li><a href="#input-data-format" id="toc-input-data-format" class="nav-link" data-scroll-target="#input-data-format">Input data format</a></li> <li><a href="#how-to-contribute-to-tsai" id="toc-how-to-contribute-to-tsai" class="nav-link" data-scroll-target="#how-to-contribute-to-tsai">How to contribute to tsai?</a></li> <li><a href="#enterprise-support-and-consulting-services" id="toc-enterprise-support-and-consulting-services" class="nav-link" data-scroll-target="#enterprise-support-and-consulting-services">Enterprise support and consulting services:</a></li> <li><a href="#citing-tsai" id="toc-citing-tsai" class="nav-link" data-scroll-target="#citing-tsai">Citing tsai</a></li> </ul> <div class="toc-actions"><ul><li><a href="https://github.com/timeseriesAI/tsai/issues/new" class="toc-action"><i class="bi bi-github"></i>Report an issue</a></li></ul></div></nav> </div>  <main class="content" id="quarto-document-content"> <header id="title-block-header" class="quarto-title-block default"> <div class="quarto-title"> <h1 class="title">tsai</h1> </div> <div class="quarto-title-meta"> </div> </header>  <div data-align="center"> <p><img src="https://github.com/timeseriesAI/tsai/blob/main/nbs/multimedia/tsai_logo.svg?raw=true" width="50%"></p> </div> <p><br> <br></p> <p><img src="https://github.com/timeseriesai/tsai/workflows/CI/badge.svg" class="img-fluid" alt="CI"> <a href="https://pypi.org/project/tsai/#description"><img src="https://img.shields.io/pypi/v/tsai?color=blue&label=pypi%20version.png" class="img-fluid" alt="PyPI"></a> <a href="https://anaconda.org/timeseriesai/tsai"><img src="https://img.shields.io/conda/vn/timeseriesai/tsai?color=brightgreen&label=conda%20version.png" class="img-fluid" alt="Conda (channel only)"></a> <a href="https://zenodo.org/badge/latestdoi/211822289"><img src="https://zenodo.org/badge/211822289.svg" class="img-fluid" alt="DOI"></a> <img src="https://img.shields.io/badge/PRs-welcome-brightgreen.svg" class="img-fluid" alt="PRs"></p> <section id="description" class="level2"> <h2 class="anchored" data-anchor-id="description">Description</h2> <blockquote class="blockquote"> <p>State-of-the-art Deep Learning library for Time Series and Sequences.</p> </blockquote> <p><code>tsai</code> is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series tasks like classification, regression, forecasting, imputation…</p> <p><code>tsai</code> is currently under active development by timeseriesAI.</p> </section> <section id="whats-new" class="level2"> <h2 class="anchored" data-anchor-id="whats-new">What’s new:</h2> <p>During the last few releases, here are some of the most significant additions to <code>tsai</code>:</p> <ul> <li><strong>New models</strong>: PatchTST (Accepted by ICLR 2023), RNN with Attention (RNNAttention, LSTMAttention, GRUAttention), TabFusionTransformer, …</li> <li><strong>New datasets</strong>: we have increased the number of datasets you can download using <code>tsai</code>: <ul> <li>128 univariate classification datasets</li> <li>30 multivariate classification datasets</li> <li>15 regression datasets</li> <li>62 forecasting datasets</li> <li>9 long term forecasting datasets</li> </ul></li> <li><strong>New tutorials</strong>: <a href="https://github.com/timeseriesAI/tsai/blob/main/tutorial_nbs/15_PatchTST_a_new_transformer_for_LTSF.ipynb">PatchTST</a>. Based on some of your requests, we are planning to release additional tutorials on data preparation and forecasting.</li> <li><strong>New functionality</strong>: sklearn-type pipeline transforms, walk-foward cross validation, reduced RAM requirements, and a lot of new functionality to perform more accurate time series forecasts.</li> <li>Pytorch 2.0 support.</li> </ul> </section> <section id="installation" class="level2"> <h2 class="anchored" data-anchor-id="installation">Installation</h2> <section id="pip-install" class="level3"> <h3 class="anchored" data-anchor-id="pip-install">Pip install</h3> <p>You can install the <strong>latest stable</strong> version from pip using:</p> <div class="sourceCode" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a>pip install tsai</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> <p>If you plan to develop tsai yourself, or want to be on the cutting edge, you can use an editable install. First install PyTorch, and then:</p> <div class="sourceCode" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>git clone https:<span class="op">//</span>github.com<span class="op">/</span>timeseriesAI<span class="op">/</span>tsai</span> <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>pip install <span class="op">-</span>e <span class="st">"tsai[dev]"</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> <p>Note: starting with tsai 0.3.0 tsai will only install hard dependencies. Other soft dependencies (which are only required for selected tasks) will not be installed by default (this is the recommended approach. If you require any of the dependencies that is not installed, tsai will ask you to install it when necessary). If you still want to install tsai with all its dependencies you can do it by running:</p> <div class="sourceCode" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>pip install tsai[extras]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> </section> <section id="conda-install" class="level3"> <h3 class="anchored" data-anchor-id="conda-install">Conda install</h3> <p>You can also install tsai using conda (note that if you replace conda with mamba the install process will be much faster and more reliable):</p> <div class="sourceCode" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>conda install <span class="op">-</span>c timeseriesai tsai</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> </section> </section> <section id="documentation" class="level2"> <h2 class="anchored" data-anchor-id="documentation">Documentation</h2> <p>Here’s the link to the <a href="https://timeseriesai.github.io/tsai/">documentation</a>.</p> </section> <section id="available-models" class="level2"> <h2 class="anchored" data-anchor-id="available-models">Available models:</h2> <p>Here’s a list with some of the state-of-the-art models available in <code>tsai</code>:</p> <ul> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/RNN.py">LSTM</a> (Hochreiter, 1997) (<a href="https://ieeexplore.ieee.org/abstract/document/6795963/">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/RNN.py">GRU</a> (Cho, 2014) (<a href="https://arxiv.org/abs/1412.3555">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/MLP.py">MLP</a> - Multilayer Perceptron (Wang, 2016) (<a href="https://arxiv.org/abs/1611.06455">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/FCN.py">FCN</a> - Fully Convolutional Network (Wang, 2016) (<a href="https://arxiv.org/abs/1611.06455">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/ResNet.py">ResNet</a> - Residual Network (Wang, 2016) (<a href="https://arxiv.org/abs/1611.06455">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/RNN_FCN.py">LSTM-FCN</a> (Karim, 2017) (<a href="https://arxiv.org/abs/1709.05206">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/RNN_FCN.py">GRU-FCN</a> (Elsayed, 2018) (<a href="https://arxiv.org/abs/1812.07683">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/mWDN.py">mWDN</a> - Multilevel wavelet decomposition network (Wang, 2018) (<a href="https://dl.acm.org/doi/abs/10.1145/3219819.3220060">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/TCN.py">TCN</a> - Temporal Convolutional Network (Bai, 2018) (<a href="https://arxiv.org/abs/1803.01271">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/RNN_FCN.py">MLSTM-FCN</a> - Multivariate LSTM-FCN (Karim, 2019) (<a href="https://www.sciencedirect.com/science/article/abs/pii/S0893608019301200">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/InceptionTime.py">InceptionTime</a> (Fawaz, 2019) (<a href="https://arxiv.org/abs/1909.04939">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/ROCKET.py">Rocket</a> (Dempster, 2019) (<a href="https://arxiv.org/abs/1910.13051">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/XceptionTime.py">XceptionTime</a> (Rahimian, 2019) (<a href="https://arxiv.org/abs/1911.03803">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/ResCNN.py">ResCNN</a> - 1D-ResCNN (Zou , 2019) (<a href="https://www.sciencedirect.com/science/article/pii/S0925231219311506">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/TabModel.py">TabModel</a> - modified from fastai’s <a href="https://docs.fast.ai/tabular.model.html#TabularModel">TabularModel</a></li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/OmniScaleCNN.py">OmniScale</a> - Omni-Scale 1D-CNN (Tang, 2020) (<a href="https://arxiv.org/abs/2002.10061">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/TST.py">TST</a> - Time Series Transformer (Zerveas, 2020) (<a href="https://dl.acm.org/doi/abs/10.1145/3447548.3467401">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/TabTransformer.py">TabTransformer</a> (Huang, 2020) (<a href="https://arxiv.org/pdf/2012.06678">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/TSiTPlus.py">TSiT</a> Adapted from ViT (Dosovitskiy, 2020) (<a href="https://arxiv.org/abs/2010.11929">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/MINIROCKET.py">MiniRocket</a> (Dempster, 2021) (<a href="https://arxiv.org/abs/2102.00457">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/XCM.py">XCM</a> - An Explainable Convolutional Neural Network (Fauvel, 2021) (<a href="https://hal.inria.fr/hal-03469487/document">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/gMLP.py">gMLP</a> - Gated Multilayer Perceptron (Liu, 2021) (<a href="https://arxiv.org/abs/2105.08050">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/TSPerceiver.py">TSPerceiver</a> - Adapted from Perceiver IO (Jaegle, 2021) (<a href="https://arxiv.org/abs/2107.14795">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/GatedTabTransformer.py">GatedTabTransformer</a> (Cholakov, 2022) (<a href="https://arxiv.org/abs/2201.00199">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/TSSequencerPlus.py">TSSequencerPlus</a> - Adapted from Sequencer (Tatsunami, 2022) (<a href="https://arxiv.org/abs/2205.01972">paper</a>)</li> <li><a href="https://github.com/timeseriesAI/tsai/blob/main/tsai/models/PatchTST.py">PatchTST</a> - (Nie, 2022) (<a href="https://arxiv.org/abs/2211.14730">paper</a>)</li> </ul> <p>plus other custom models like: TransformerModel, LSTMAttention, GRUAttention, …</p> </section> <section id="how-to-start-using-tsai" class="level2"> <h2 class="anchored" data-anchor-id="how-to-start-using-tsai">How to start using tsai?</h2> <p>To get to know the tsai package, we’d suggest you start with this notebook in Google Colab: <strong><a href="https://colab.research.google.com/github/timeseriesAI/tsai/blob/master/tutorial_nbs/01_Intro_to_Time_Series_Classification.ipynb">01_Intro_to_Time_Series_Classification</a></strong> It provides an overview of a time series classification task.</p> <p>We have also develop many other <a href="https://github.com/timeseriesAI/tsai/tree/main/tutorial_nbs">tutorial notebooks</a>.</p> <p>To use tsai in your own notebooks, the only thing you need to do after you have installed the package is to run this:</p> <div class="sourceCode" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsai.<span class="bu">all</span> <span class="im">import</span> <span class="op">*</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> </section> <section id="examples" class="level2"> <h2 class="anchored" data-anchor-id="examples">Examples</h2> <p>These are just a few examples of how you can use <code>tsai</code>:</p> <section id="binary-univariate-classification" class="level3"> <h3 class="anchored" data-anchor-id="binary-univariate-classification">Binary, univariate classification</h3> <p><strong>Training:</strong></p> <div class="sourceCode" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsai.basics <span class="im">import</span> <span class="op">*</span></span> <span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a></span> <span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>X, y, splits <span class="op">=</span> get_classification_data(<span class="st">'ECG200'</span>, split_data<span class="op">=</span><span class="va">False</span>)</span> <span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>tfms <span class="op">=</span> [<span class="va">None</span>, TSClassification()]</span> <span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>batch_tfms <span class="op">=</span> TSStandardize()</span> <span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a>clf <span class="op">=</span> TSClassifier(X, y, splits<span class="op">=</span>splits, path<span class="op">=</span><span class="st">'models'</span>, arch<span class="op">=</span><span class="st">"InceptionTimePlus"</span>, tfms<span class="op">=</span>tfms, batch_tfms<span class="op">=</span>batch_tfms, metrics<span class="op">=</span>accuracy, cbs<span class="op">=</span>ShowGraph())</span> <span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a>clf.fit_one_cycle(<span class="dv">100</span>, <span class="fl">3e-4</span>)</span> <span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a>clf.export(<span class="st">"clf.pkl"</span>) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> <p><strong>Inference:</strong></p> <div class="sourceCode" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsai.inference <span class="im">import</span> load_learner</span> <span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a></span> <span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>clf <span class="op">=</span> load_learner(<span class="st">"models/clf.pkl"</span>)</span> <span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>probas, target, preds <span class="op">=</span> clf.get_X_preds(X[splits[<span class="dv">1</span>]], y[splits[<span class="dv">1</span>]])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> </section> <section id="multi-class-multivariate-classification" class="level3"> <h3 class="anchored" data-anchor-id="multi-class-multivariate-classification">Multi-class, multivariate classification</h3> <p><strong>Training:</strong></p> <div class="sourceCode" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsai.basics <span class="im">import</span> <span class="op">*</span></span> <span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a></span> <span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>X, y, splits <span class="op">=</span> get_classification_data(<span class="st">'LSST'</span>, split_data<span class="op">=</span><span class="va">False</span>)</span> <span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>tfms <span class="op">=</span> [<span class="va">None</span>, TSClassification()]</span> <span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a>batch_tfms <span class="op">=</span> TSStandardize(by_sample<span class="op">=</span><span class="va">True</span>)</span> <span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a>mv_clf <span class="op">=</span> TSClassifier(X, y, splits<span class="op">=</span>splits, path<span class="op">=</span><span class="st">'models'</span>, arch<span class="op">=</span><span class="st">"InceptionTimePlus"</span>, tfms<span class="op">=</span>tfms, batch_tfms<span class="op">=</span>batch_tfms, metrics<span class="op">=</span>accuracy, cbs<span class="op">=</span>ShowGraph())</span> <span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a>mv_clf.fit_one_cycle(<span class="dv">10</span>, <span class="fl">1e-2</span>)</span> <span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a>mv_clf.export(<span class="st">"mv_clf.pkl"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> <p><strong>Inference:</strong></p> <div class="sourceCode" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsai.inference <span class="im">import</span> load_learner</span> <span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a></span> <span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>mv_clf <span class="op">=</span> load_learner(<span class="st">"models/mv_clf.pkl"</span>)</span> <span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>probas, target, preds <span class="op">=</span> mv_clf.get_X_preds(X[splits[<span class="dv">1</span>]], y[splits[<span class="dv">1</span>]])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> </section> <section id="multivariate-regression" class="level3"> <h3 class="anchored" data-anchor-id="multivariate-regression">Multivariate Regression</h3> <p><strong>Training:</strong></p> <div class="sourceCode" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsai.basics <span class="im">import</span> <span class="op">*</span></span> <span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a></span> <span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>X, y, splits <span class="op">=</span> get_regression_data(<span class="st">'AppliancesEnergy'</span>, split_data<span class="op">=</span><span class="va">False</span>)</span> <span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>tfms <span class="op">=</span> [<span class="va">None</span>, TSRegression()]</span> <span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a>batch_tfms <span class="op">=</span> TSStandardize(by_sample<span class="op">=</span><span class="va">True</span>)</span> <span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a>reg <span class="op">=</span> TSRegressor(X, y, splits<span class="op">=</span>splits, path<span class="op">=</span><span class="st">'models'</span>, arch<span class="op">=</span><span class="st">"TSTPlus"</span>, tfms<span class="op">=</span>tfms, batch_tfms<span class="op">=</span>batch_tfms, metrics<span class="op">=</span>rmse, cbs<span class="op">=</span>ShowGraph(), verbose<span class="op">=</span><span class="va">True</span>)</span> <span id="cb10-7"><a href="#cb10-7" aria-hidden="true" tabindex="-1"></a>reg.fit_one_cycle(<span class="dv">100</span>, <span class="fl">3e-4</span>)</span> <span id="cb10-8"><a href="#cb10-8" aria-hidden="true" tabindex="-1"></a>reg.export(<span class="st">"reg.pkl"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> <p><strong>Inference:</strong></p> <div class="sourceCode" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsai.inference <span class="im">import</span> load_learner</span> <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a></span> <span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>reg <span class="op">=</span> load_learner(<span class="st">"models/reg.pkl"</span>)</span> <span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a>raw_preds, target, preds <span class="op">=</span> reg.get_X_preds(X[splits[<span class="dv">1</span>]], y[splits[<span class="dv">1</span>]])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> <p>The ROCKETs (RocketClassifier, RocketRegressor, MiniRocketClassifier, MiniRocketRegressor, MiniRocketVotingClassifier or MiniRocketVotingRegressor) are somewhat different models. They are not actually deep learning models (although they use convolutions) and are used in a different way.</p> <p>⚠️ You’ll also need to install sktime to be able to use them. You can install it separately:</p> <div class="sourceCode" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>pip install sktime</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> <p>or use:</p> <div class="sourceCode" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>pip install tsai[extras]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> <p><strong>Training:</strong></p> <div class="sourceCode" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sklearn.metrics <span class="im">import</span> mean_squared_error, make_scorer</span> <span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsai.data.external <span class="im">import</span> get_Monash_regression_data</span> <span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsai.models.MINIROCKET <span class="im">import</span> MiniRocketRegressor</span> <span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a></span> <span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a>X_train, y_train, <span class="op">*</span>_ <span class="op">=</span> get_Monash_regression_data(<span class="st">'AppliancesEnergy'</span>)</span> <span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a>rmse_scorer <span class="op">=</span> make_scorer(mean_squared_error, greater_is_better<span class="op">=</span><span class="va">False</span>)</span> <span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a>reg <span class="op">=</span> MiniRocketRegressor(scoring<span class="op">=</span>rmse_scorer)</span> <span id="cb14-8"><a href="#cb14-8" aria-hidden="true" tabindex="-1"></a>reg.fit(X_train, y_train)</span> <span id="cb14-9"><a href="#cb14-9" aria-hidden="true" tabindex="-1"></a>reg.save(<span class="st">'MiniRocketRegressor'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> <p><strong>Inference:</strong></p> <div class="sourceCode" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sklearn.metrics <span class="im">import</span> mean_squared_error</span> <span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsai.data.external <span class="im">import</span> get_Monash_regression_data</span> <span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsai.models.MINIROCKET <span class="im">import</span> load_minirocket</span> <span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a></span> <span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a><span class="op">*</span>_, X_test, y_test <span class="op">=</span> get_Monash_regression_data(<span class="st">'AppliancesEnergy'</span>)</span> <span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a>reg <span class="op">=</span> load_minirocket(<span class="st">'MiniRocketRegressor'</span>)</span> <span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> reg.predict(X_test)</span> <span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a>mean_squared_error(y_test, y_pred, squared<span class="op">=</span><span class="va">False</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> </section> <section id="forecasting" class="level3"> <h3 class="anchored" data-anchor-id="forecasting">Forecasting</h3> <p>You can use tsai for forecast in the following scenarios:</p> <ul> <li>univariate or multivariate time series input</li> <li>univariate or multivariate time series output</li> <li>single or multi-step ahead</li> </ul> <p>You’ll need to: * prepare X (time series input) and the target y (see <a href="https://timeseriesai.github.io/tsai/data.preparation.html">documentation</a>) * select PatchTST or one of tsai’s models ending in Plus (TSTPlus, InceptionTimePlus, TSiTPlus, etc). The model will auto-configure a head to yield an output with the same shape as the target input y.</p> <section id="single-step" class="level4"> <h4 class="anchored" data-anchor-id="single-step">Single step</h4> <p><strong>Training:</strong></p> <div class="sourceCode" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsai.basics <span class="im">import</span> <span class="op">*</span></span> <span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a></span> <span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>ts <span class="op">=</span> get_forecasting_time_series(<span class="st">"Sunspots"</span>).values</span> <span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a>X, y <span class="op">=</span> SlidingWindow(<span class="dv">60</span>, horizon<span class="op">=</span><span class="dv">1</span>)(ts)</span> <span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a>splits <span class="op">=</span> TimeSplitter(<span class="dv">235</span>)(y) </span> <span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a>tfms <span class="op">=</span> [<span class="va">None</span>, TSForecasting()]</span> <span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a>batch_tfms <span class="op">=</span> TSStandardize()</span> <span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a>fcst <span class="op">=</span> TSForecaster(X, y, splits<span class="op">=</span>splits, path<span class="op">=</span><span class="st">'models'</span>, tfms<span class="op">=</span>tfms, batch_tfms<span class="op">=</span>batch_tfms, bs<span class="op">=</span><span class="dv">512</span>, arch<span class="op">=</span><span class="st">"TSTPlus"</span>, metrics<span class="op">=</span>mae, cbs<span class="op">=</span>ShowGraph())</span> <span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a>fcst.fit_one_cycle(<span class="dv">50</span>, <span class="fl">1e-3</span>)</span> <span id="cb16-10"><a href="#cb16-10" aria-hidden="true" tabindex="-1"></a>fcst.export(<span class="st">"fcst.pkl"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> <p><strong>Inference:</strong></p> <div class="sourceCode" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsai.inference <span class="im">import</span> load_learner</span> <span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a></span> <span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>fcst <span class="op">=</span> load_learner(<span class="st">"models/fcst.pkl"</span>, cpu<span class="op">=</span><span class="va">False</span>)</span> <span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a>raw_preds, target, preds <span class="op">=</span> fcst.get_X_preds(X[splits[<span class="dv">1</span>]], y[splits[<span class="dv">1</span>]])</span> <span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a>raw_preds.shape</span> <span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a><span class="co"># torch.Size([235, 1])</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> </section> <section id="multi-step" class="level4"> <h4 class="anchored" data-anchor-id="multi-step">Multi-step</h4> <p>This example show how to build a 3-step ahead univariate forecast.</p> <p><strong>Training:</strong></p> <div class="sourceCode" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsai.basics <span class="im">import</span> <span class="op">*</span></span> <span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a></span> <span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>ts <span class="op">=</span> get_forecasting_time_series(<span class="st">"Sunspots"</span>).values</span> <span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a>X, y <span class="op">=</span> SlidingWindow(<span class="dv">60</span>, horizon<span class="op">=</span><span class="dv">3</span>)(ts)</span> <span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a>splits <span class="op">=</span> TimeSplitter(<span class="dv">235</span>, fcst_horizon<span class="op">=</span><span class="dv">3</span>)(y) </span> <span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a>tfms <span class="op">=</span> [<span class="va">None</span>, TSForecasting()]</span> <span id="cb18-7"><a href="#cb18-7" aria-hidden="true" tabindex="-1"></a>batch_tfms <span class="op">=</span> TSStandardize()</span> <span id="cb18-8"><a href="#cb18-8" aria-hidden="true" tabindex="-1"></a>fcst <span class="op">=</span> TSForecaster(X, y, splits<span class="op">=</span>splits, path<span class="op">=</span><span class="st">'models'</span>, tfms<span class="op">=</span>tfms, batch_tfms<span class="op">=</span>batch_tfms, bs<span class="op">=</span><span class="dv">512</span>, arch<span class="op">=</span><span class="st">"TSTPlus"</span>, metrics<span class="op">=</span>mae, cbs<span class="op">=</span>ShowGraph())</span> <span id="cb18-9"><a href="#cb18-9" aria-hidden="true" tabindex="-1"></a>fcst.fit_one_cycle(<span class="dv">50</span>, <span class="fl">1e-3</span>)</span> <span id="cb18-10"><a href="#cb18-10" aria-hidden="true" tabindex="-1"></a>fcst.export(<span class="st">"fcst.pkl"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> <p><strong>Inference:</strong></p> <div class="sourceCode" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsai.inference <span class="im">import</span> load_learner</span> <span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>fcst <span class="op">=</span> load_learner(<span class="st">"models/fcst.pkl"</span>, cpu<span class="op">=</span><span class="va">False</span>)</span> <span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a>raw_preds, target, preds <span class="op">=</span> fcst.get_X_preds(X[splits[<span class="dv">1</span>]], y[splits[<span class="dv">1</span>]])</span> <span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a>raw_preds.shape</span> <span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a><span class="co"># torch.Size([235, 3])</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div> </section> </section> </section> <section id="input-data-format" class="level2"> <h2 class="anchored" data-anchor-id="input-data-format">Input data format</h2> <p>The input format for all time series models and image models in tsai is the same. An np.ndarray (or array-like object like zarr, etc) with 3 dimensions:</p> <p><strong>[# samples x # variables x sequence length]</strong></p> <p>The input format for tabular models in tsai (like TabModel, TabTransformer and TabFusionTransformer) is a pandas dataframe. See <a href="https://timeseriesai.github.io/tsai/models.TabModel.html">example</a>.</p> </section> <section id="how-to-contribute-to-tsai" class="level2"> <h2 class="anchored" data-anchor-id="how-to-contribute-to-tsai">How to contribute to tsai?</h2> <p>We welcome contributions of all kinds. Development of enhancements, bug fixes, documentation, tutorial notebooks, …</p> <p>We have created a guide to help you start contributing to tsai. You can read it <a href="https://github.com/timeseriesAI/tsai/blob/main/CONTRIBUTING.md">here</a>.</p> </section> <section id="enterprise-support-and-consulting-services" class="level2"> <h2 class="anchored" data-anchor-id="enterprise-support-and-consulting-services">Enterprise support and consulting services:</h2> <p>Want to make the most out of timeseriesAI/tsai in a professional setting? Let us help. Send us an email to learn more: info@timeseriesai.co</p> </section> <section id="citing-tsai" class="level2"> <h2 class="anchored" data-anchor-id="citing-tsai">Citing tsai</h2> <p>If you use tsai in your research please use the following BibTeX entry:</p> <pre class="text"><code>@Misc{tsai, author = {Ignacio Oguiza}, title = {tsai - A state-of-the-art deep learning library for time series and sequential data}, howpublished = {Github}, year = {2023}, url = {https://github.com/timeseriesAI/tsai} }</code></pre> </section> </main>  <script id="quarto-html-after-body" type="application/javascript"> window.document.addEventListener("DOMContentLoaded", function (event) { const toggleBodyColorMode = (bsSheetEl) => { const mode = bsSheetEl.getAttribute("data-mode"); const bodyEl = window.document.querySelector("body"); if (mode === "dark") { bodyEl.classList.add("quarto-dark"); bodyEl.classList.remove("quarto-light"); } else { bodyEl.classList.add("quarto-light"); bodyEl.classList.remove("quarto-dark"); 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