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            "Development Status :: 3 - Alpha",
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        "description": "[![Build Status](https://travis-ci.org/johannfaouzi/pyts.svg?branch=master)](https://travis-ci.org/johannfaouzi/pyts)\n[![Build Status](https://img.shields.io/appveyor/ci/johannfaouzi/pyts/master.svg)](https://ci.appveyor.com/project/johannfaouzi/pyts)\n[![Documentation Status](https://readthedocs.org/projects/pyts/badge/?version=latest)](https://pyts.readthedocs.io/en/latest/?badge=latest)\n[![Codecov](https://codecov.io/gh/johannfaouzi/pyts/branch/master/graph/badge.svg)](https://codecov.io/gh/johannfaouzi/pyts)\n[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyts.svg)](https://img.shields.io/pypi/pyversions/pyts.svg)\n[![PyPI version](https://badge.fury.io/py/pyts.svg)](https://badge.fury.io/py/pyts)\n[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/johannfaouzi/pyts.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/johannfaouzi/pyts/context:python)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1244152.svg)](https://doi.org/10.5281/zenodo.1244152)\n\n## pyts: a Python package for time series classification\n\npyts is a Python package for time series classification. It\naims to make time series classification easily accessible by providing\npreprocessing and utility tools, and implementations of\nstate-of-the-art algorithms. Most of these algorithms transform time series,\nthus pyts provides several tools to perform these transformations.\n\n\n### Installation\n\n#### Dependencies\n\npyts requires:\n\n- Python (>= 3.5)\n- NumPy (>= 1.15.4)\n- SciPy (>= 1.3.0)\n- Scikit-Learn (>=0.20.1)\n- Numba (>=0.41.0)\n\nTo run the examples Matplotlib (>=2.0.0) is required.\n\n\n#### User installation\n\nIf you already have a working installation of numpy, scipy, scikit-learn and\nnumba, you can easily install pyts using ``pip``\n\n    pip install pyts\n\nor ``conda`` via the ``conda-forge`` channel\n\n  conda install -c conda-forge pyts\n\nYou can also get the latest version of pyts by cloning the repository\n\n    git clone https://github.com/johannfaouzi/pyts.git\n    cd pyts\n    pip install .\n\n\n#### Testing\n\nAfter installation, you can launch the test suite from outside the source\ndirectory using pytest:\n\n    pytest pyts\n\n\n### Changelog\n\nSee the [changelog](https://pyts.readthedocs.io/en/latest/changelog.html)\nfor a history of notable changes to pyts.\n\n### Development\n\nThe development of this package is in line with the one of the scikit-learn\ncommunity. Therefore, you can refer to their\n[Development Guide](https://scikit-learn.org/stable/developers/). A slight\ndifference is the use of Numba instead of Cython for optimization.\n\n### Documentation\n\nThe section below gives some information about the implemented algorithms in pyts.\nFor more information, please have a look at the\n[HTML documentation available via ReadTheDocs](https://pyts.readthedocs.io/en/latest/).\n\n### Implemented features\n\npyts consists of the following modules:\n\n- `approximation`: This module provides implementations of algorithms that\napproximate time series. Implemented algorithms are\n[Piecewise Aggregate Approximation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.PiecewiseAggregateApproximation.html#),\n[Symbolic Aggregate approXimation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.SymbolicAggregateApproximation.html#),\n[Discrete Fourier Transform](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.DiscreteFourierTransform.html#),\n[Multiple Coefficient Binning](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.MultipleCoefficientBinning.html#) and\n[Symbolic Fourier Approximation](https://pyts.readthedocs.io/en/latest/generated/pyts.approximation.SymbolicFourierApproximation.html#).\n\n- `bag_of_words`: This module consists of a class\n[BagOfWords](https://pyts.readthedocs.io/en/latest/generated/pyts.bag_of_words.BagOfWords.html#)\nthat transforms time series into bags of words. This approach is quite common\nin time series classification.\n\n- `classification`: This module provides implementations of algorithms that\ncan classify time series. Implemented algorithms are\n[KNeighborsClassifier](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.KNeighborsClassifier.html#),\n[SAXVSM](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.SAXVSM.html#) and\n[BOSSVS](https://pyts.readthedocs.io/en/latest/generated/pyts.classification.BOSSVS.html#).\n\n- `datasets`: This module provides utilities to make or load toy datasets,\nas well as fetching datasets from the\n[UEA & UCR Time Series Classification Repository](http://www.timeseriesclassification.com).\n\n- `decomposition`: This module provides implementations of algorithms that\ndecompose a time series into several time series. The only implemented\nalgorithm is\n[Singular Spectrum Analysis](https://pyts.readthedocs.io/en/latest/generated/pyts.decomposition.SingularSpectrumAnalysis.html#).\n\n- `image`: This module provides implementations of algorithms that transform\ntime series into images. Implemented algorithms are\n[Recurrence Plot](https://pyts.readthedocs.io/en/latest/generated/pyts.image.RecurrencePlot.html#),\n[Gramian Angular Field](https://pyts.readthedocs.io/en/latest/generated/pyts.image.GramianAngularField.html#) and\n[Markov Transition Field](https://pyts.readthedocs.io/en/latest/generated/pyts.image.MarkovTransitionField.html#).\n\n- `metrics`: This module provides implementations of metrics that are specific\nto time series. Implemented metrics are\n[Dynamic Time Warping](https://pyts.readthedocs.io/en/latest/generated/pyts.metrics.dtw.html#)\nwith several variants and the\n[BOSS](https://pyts.readthedocs.io/en/latest/generated/pyts.metrics.boss.html#)\nmetric.\n\n- `multivariate`: This modules provides utilities to deal with multivariate\ntime series. Available tools are\n[MultivariateTransformer](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.transformation.MultivariateTransformer.html) and\n[MultivariateClassifier](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.classification.MultivariateClassifier.html)\nto transform and classify multivariate time series using tools for univariate\ntime series respectively, as well as\n[JointRecurrencePlot](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.image.JointRecurrencePlot.html) and\n[WEASEL+MUSE](https://pyts.readthedocs.io/en/latest/generated/pyts.multivariate.transformation.WEASELMUSE.html).\n\n- `preprocessing`: This module provides most of the scikit-learn preprocessing\ntools but applied sample-wise (i.e. to each time series independently) instead\nof feature-wise, as well as an\n[imputer](https://pyts.readthedocs.io/en/latest/generated/pyts.preprocessing.InterpolationImputer.html#)\nof missing values using interpolation. More information is available at the\n[pyts.preprocessing API documentation](https://pyts.readthedocs.io/en/latest/api.html#module-pyts.preprocessing).\n\n- `transformation`: This module provides implementations of algorithms that\ntransform a data set of time series with shape `(n_samples, n_timestamps)` into\na data set with shape `(n_samples, n_features)`. Implemented algorithms are\n[BOSS](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.BOSS.html#) and\n[WEASEL](https://pyts.readthedocs.io/en/latest/generated/pyts.transformation.WEASEL.html#).\n\n- `utils`: a simple module with\n[utility functions](https://pyts.readthedocs.io/en/latest/api.html#module-pyts.utils).\n\n\n",
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