# tslearn **Repository Path**: mirrors_lepy/tslearn ## Basic Information - **Project Name**: tslearn - **Description**: A machine learning toolkit dedicated to time-series data - **Primary Language**: Unknown - **License**: BSD-2-Clause - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-09-25 - **Last Updated**: 2025-07-02 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

tslearn

The machine learning toolkit for time series analysis in Python

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| Section | Description | |-|-| | [Installation](#installation) | Installing the dependencies and tslearn | | [Getting started](#getting-started) | A quick introduction on how to use tslearn | | [Available features](#available-features) | An extensive overview of tslearn's functionalities | | [Documentation](#documentation) | A link to our API reference and a gallery of examples | | [Contributing](#contributing) | A guide for heroes willing to contribute | | [Citation](#referencing-tslearn) | A citation for tslearn for scholarly articles | ## Installation There are different alternatives to install tslearn: * PyPi: `python -m pip install tslearn` * Conda: `conda install -c conda-forge tslearn` * Git: `python -m pip install https://github.com/tslearn-team/tslearn/archive/master.zip` In order for the installation to be successful, the required dependencies must be installed. For a more detailed guide on how to install tslearn, please see the [Documentation](https://tslearn.readthedocs.io/en/stable/?badge=stable#installation). ## Getting started ### 1. Getting the data in the right format tslearn expects a time series dataset to be formatted as a 3D `numpy` array. The three dimensions correspond to the number of time series, the number of measurements per time series and the number of dimensions respectively (`n_ts, max_sz, d`). In order to get the data in the right format, different solutions exist: * [You can use the utility functions such as `to_time_series_dataset`.](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.utils.html#module-tslearn.utils) * [You can convert from other popular time series toolkits in Python.](https://tslearn.readthedocs.io/en/stable/integration_other_software.html) * [You can load any of the UCR datasets in the required format.](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.datasets.html#module-tslearn.datasets) * [You can generate synthetic data using the `generators` module.](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.generators.html#module-tslearn.generators) It should further be noted that tslearn [supports variable-length timeseries](https://tslearn.readthedocs.io/en/stable/variablelength.html). ```python3 >>> from tslearn.utils import to_time_series_dataset >>> my_first_time_series = [1, 3, 4, 2] >>> my_second_time_series = [1, 2, 4, 2] >>> my_third_time_series = [1, 2, 4, 2, 2] >>> X = to_time_series_dataset([my_first_time_series, my_second_time_series, my_third_time_series]) >>> y = [0, 1, 1] ``` ### 2. Data preprocessing and transformations Optionally, tslearn has several utilities to preprocess the data. In order to facilitate the convergence of different algorithms, you can [scale time series](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.preprocessing.html#module-tslearn.preprocessing). Alternatively, in order to speed up training times, one can [resample](https://tslearn.readthedocs.io/en/stable/gen_modules/preprocessing/tslearn.preprocessing.TimeSeriesResampler.html#tslearn.preprocessing.TimeSeriesResampler) the data or apply a [piece-wise transformation](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.piecewise.html#module-tslearn.piecewise). ```python3 >>> from tslearn.preprocessing import TimeSeriesScalerMinMax >>> X_scaled = TimeSeriesScalerMinMax().fit_transform(X) >>> print(X_scaled) [[[0.] [0.667] [1.] [0.333] [nan]] [[0.] [0.333] [1.] [0.333] [nan]] [[0.] [0.333] [1.] [0.333] [0.333]]] ``` ### 3. Training a model After getting the data in the right format, a model can be trained. Depending on the use case, tslearn supports different tasks: classification, clustering and regression. For an extensive overview of possibilities, check out our [gallery of examples](https://tslearn.readthedocs.io/en/stable/auto_examples/index.html). ```python3 >>> from tslearn.neighbors import KNeighborsTimeSeriesClassifier >>> knn = KNeighborsTimeSeriesClassifier(n_neighbors=1) >>> knn.fit(X_scaled, y) >>> print(knn.predict(X_scaled)) [0 1 1] ``` As can be seen, the models in tslearn follow the same API as those of the well-known scikit-learn. Moreover, they are fully compatible with it, allowing to use different scikit-learn utilities such as [hyper-parameter tuning and pipelines](https://tslearn.readthedocs.io/en/stable/auto_examples/plot_knnts_sklearn.html#sphx-glr-auto-examples-plot-knnts-sklearn-py). ### 4. More analyses tslearn further allows to perform all different types of analysis. Examples include [calculating barycenters](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.barycenters.html#module-tslearn.barycenters) of a group of time series or calculate the distances between time series using a [variety of distance metrics](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.metrics.html#module-tslearn.metrics). ## Available features | data | processing | clustering | classification | regression | metrics | |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------| | [UCR Datasets](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.datasets.html#module-tslearn.datasets) | [Scaling](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.preprocessing.html#module-tslearn.preprocessing) | [TimeSeriesKMeans](https://tslearn.readthedocs.io/en/stable/gen_modules/clustering/tslearn.clustering.TimeSeriesKMeans.html#tslearn.clustering.TimeSeriesKMeans) | [KNN Classifier](https://tslearn.readthedocs.io/en/stable/gen_modules/neighbors/tslearn.neighbors.KNeighborsTimeSeriesClassifier.html#tslearn.neighbors.KNeighborsTimeSeriesClassifier) | [KNN Regressor](https://tslearn.readthedocs.io/en/stable/gen_modules/neighbors/tslearn.neighbors.KNeighborsTimeSeriesRegressor.html#tslearn.neighbors.KNeighborsTimeSeriesRegressor) | [Dynamic Time Warping](https://tslearn.readthedocs.io/en/stable/gen_modules/metrics/tslearn.metrics.dtw.html#tslearn.metrics.dtw) | | [Generators](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.generators.html#module-tslearn.generators) | [Piecewise](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.piecewise.html#module-tslearn.piecewise) | [KShape](https://tslearn.readthedocs.io/en/stable/gen_modules/clustering/tslearn.clustering.KShape.html#tslearn.clustering.KShape) | [TimeSeriesSVC](https://tslearn.readthedocs.io/en/stable/gen_modules/svm/tslearn.svm.TimeSeriesSVC.html#tslearn.svm.TimeSeriesSVC) | [TimeSeriesSVR](https://tslearn.readthedocs.io/en/stable/gen_modules/svm/tslearn.svm.TimeSeriesSVR.html#tslearn.svm.TimeSeriesSVR) | [Global Alignment Kernel](https://tslearn.readthedocs.io/en/stable/gen_modules/metrics/tslearn.metrics.gak.html#tslearn.metrics.gak) | | Conversion([1](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.utils.html#module-tslearn.utils), [2](https://tslearn.readthedocs.io/en/stable/integration_other_software.html)) | | [KernelKmeans](https://tslearn.readthedocs.io/en/stable/gen_modules/clustering/tslearn.clustering.KernelKMeans.html#tslearn.clustering.KernelKMeans) | [ShapeletModel](https://tslearn.readthedocs.io/en/stable/gen_modules/shapelets/tslearn.shapelets.ShapeletModel.html#tslearn.shapelets.ShapeletModel) | | [Barycenters](https://tslearn.readthedocs.io/en/stable/gen_modules/tslearn.barycenters.html#module-tslearn.barycenters) | ## Documentation The documentation, including a gallery of examples, is hosted at [readthedocs](http://tslearn.readthedocs.io/en/stable/index.html). ## Contributing If you would like to contribute to `tslearn`, please have a look at [our contribution guidelines](CONTRIBUTING.md). A list of interesting TODO's can be found [here](https://github.com/tslearn-team/tslearn/issues?utf8=✓&q=is%3Aissue%20is%3Aopen%20label%3A%22new%20feature%22%20). **If you want other ML methods for time series to be added to this TODO list, do not hesitate to [open an issue](https://github.com/tslearn-team/tslearn/issues/new/choose)!** ## Referencing tslearn If you use `tslearn` in a scientific publication, we would appreciate citations: ```bibtex @misc{tslearn, title={tslearn: A machine learning toolkit dedicated to time-series data}, author={Romain Tavenard and Johann Faouzi and Gilles Vandewiele and Felix Divo and Guillaume Androz and Chester Holtz and Marie Payne and Roman Yurchak and Marc Ru{\ss}wurm and Kushal Kolar and Eli Woods}, year={2017}, note={\url{https://github.com/tslearn-team/tslearn}} } ``` #### Acknowledgments Authors would like to thank Mathieu Blondel for providing code for [Kernel k-means](https://gist.github.com/mblondel/6230787) and [Soft-DTW](https://github.com/mblondel/soft-dtw).