# tslearn（镜像）

**Repository Path**: lewous/tslearn

## Basic Information

- **Project Name**: tslearn（镜像）
- **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**: 2020-03-17
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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[![Documentation Status](https://readthedocs.org/projects/tslearn/badge/?version=latest)](http://tslearn.readthedocs.io/en/latest/?badge=latest)
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`tslearn` is a Python package that provides machine learning tools for the analysis of time series.
This package builds on `scikit-learn`, `numpy` and `scipy` libraries.

If you would like to contribute to `tslearn`, please have a look at [our contribution guidelines](CONTRIBUTING.md).

# Dependencies

```
Cython
numpy
numba
scipy
scikit-learn
joblib
numba
```

If you plan to use the `shapelets` module, `keras` and `tensorflow` should also be installed.
`h5py` is required for reading or writing models using the hdf5 file format.

# Installation

## Using conda

The easiest way to install `tslearn` is probably via `conda`:
```bash
conda install -c conda-forge tslearn
```

## Using PyPI

### Pre-requisites 

When using PyPI, C++ build tools should be available to perform installation.

Using `pip` should also work fine:
```bash
pip install tslearn
```

In this case, you should have `numpy`, `cython` and C++ build tools available
at build time.

## Using latest github-hosted version

If you want to get `tslearn`'s latest version, you can refer to the repository 
hosted at github:
```bash
pip install git+https://github.com/rtavenar/tslearn.git
```

## Troubleshooting

It seems on some platforms `Cython` dependency does not install properly.
If you experiment such an issue, try installing it with the following command:

```bash
pip install cython
```

before you start installing `tslearn`.
If it still does not work, we suggest you switch to `conda` installation.

# Documentation and API reference

The documentation, including a gallery of examples, is hosted at 
[readthedocs](http://tslearn.readthedocs.io/en/latest/index.html).

# Already available

* A `generators` module provides Random Walks generators
* A `datasets` module provides access to the famous UCR/UEA datasets through the `UCR_UEA_datasets` class
* A `preprocessing` module provides standard time series scalers
* A `metrics` module provides:
  * Dynamic Time Warping (DTW) (with Sakoe-Chiba band and Itakura parallelogram variants)
  * LB_Keogh
  * Global Alignment Kernel
  * Soft-DTW from Cuturi and Blondel
* A `neighbors` module includes nearest neighbor algorithms to be used with time series
* An `svm` module includes Support Vector Machine algorithms with:
  * Standard kernels offered in `sklearn` (with adequate array reshaping done for you)
  * Global Alignment Kernel
* A `clustering` module includes the following time series clustering algorithms:
  * Standard Euclidean k-means (with adequate array reshaping done for you)
    * Based on `tslearn.barycenters`
  * DBA k-means from Petitjean _et al._
    * Based on `tslearn.barycenters` that offers DBA facility that could be used for other applications than just
    k-means
  * Global Alignment kernel k-means
  * KShape clustering from Paparizzos and Gravano
  * Soft-DTW k-means from Cuturi and Blondel
    * Based on `tslearn.barycenters` that offers Soft-DTW barycenter computation
  * It also provides a way to compute the silhouette coefficient for given clustering and metric
* A `shapelets` module includes an efficient implementation of the Learning Time-Series method from Grabocka _et al._
  * **Warning:** to use the `shapelets` module, two extra dependencies are required: `keras` and `tensorflow`
* A `piecewise` module includes standard time series transformations, as well as the corresponding distances:
  * Piecewise Aggregate Approximation (PAA)
  * Symbolic Aggregate approXimation (SAX)
  * 1d-Symbolic Aggregate approXimation (1d-SAX)

# TODO list

Have a look [there](https://github.com/rtavenar/tslearn/issues?utf8=✓&q=is%3Aissue%20is%3Aopen%20label%3A%22new%20feature%22%20) for a list of suggested features.
**If you want other ML methods for time series to be added to this TODO list, do not hesitate to open an issue!** See [our contribution guidelines](CONTRIBUTING.md) for more information about how to proceed.

# 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) (both
distributed under BSD license) that are used in the `clustering` and `metrics` modules of this library.

# 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/rtavenar/tslearn}}
}
```