Metadata-Version: 2.1
Name: giotto-time
Version: 0.2.1
Summary: Toolbox for Time Series analysis and integration with Machine Learning.
Home-page: https://github.com/giotto-ai/giotto-time
Maintainer: Alessio Baccelli
Maintainer-email: maintainers@giotto.ai
License: AGPLv3
Download-URL: https://github.com/giotto-ai/giotto-time/tarball/v0.0a0
Description: .. image:: https://www.giotto.ai/static/vector/logo-time.svg
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        giotto-time
        ===========
        
        giotto-time is a machine learning based time series forecasting toolbox in Python.
        It is part of the `Giotto <https://github.com/giotto-ai>`_ collection of open-source projects and aims to provide
        feature extraction, analysis, causality testing and forecasting models based on
        `scikit-learn <https://scikit-learn.org/stable/>`_ API.
        
        License
        -------
        
        giotto-time is distributed under the AGPLv3 `license <https://github.com/giotto-ai/giotto-time/blob/master/LICENSE>`_.
        If you need a different distribution license, please contact the L2F team at business@l2f.ch.
        
        Documentation
        -------------
        
        - API reference (stable release): https://docs-time.giotto.ai
        
        Getting started
        ---------------
        
        Get started with giotto-time by following the installation steps below.
        Simple tutorials and real-world use cases can be found in example folder as notebooks.
        
        Installation
        ------------
        
        User installation
        ~~~~~~~~~~~~~~~~~
        
        Run this command in your favourite python environment  ::
        
            pip install giotto-time
        
        Developer installation
        ~~~~~~~~~~~~~~~~~~~~~~
        
        Get the latest state of the source code with the command
        
        .. code-block:: bash
        
            git clone https://github.com/giotto-ai/giotto-time.git
            cd giotto-time
            pip install -e ".[tests, doc]"
        
        Example
        -------
        
        .. code-block:: python
        
            from gtime import *
            from gtime.feature_extraction import *
            import pandas as pd
            import numpy as np
            from sklearn.linear_model import LinearRegression
        
            # Create random DataFrame with DatetimeIndex
            X_dt = pd.DataFrame(np.random.randint(4, size=(20)),
                                index=pd.date_range("2019-12-20", "2020-01-08"),
                                columns=['time_series'])
        
            # Convert the DatetimeIndex to PeriodIndex and create y matrix
            X = preprocessing.TimeSeriesPreparation().transform(X_dt)
            y = model_selection.horizon_shift(X, horizon=2)
        
            # Create some features
            cal = feature_generation.Calendar(region="europe", country="Switzerland", kernel=np.array([1, 2]))
            X_f = compose.FeatureCreation(
                [('s_2', Shift(2), ['time_series']),
                 ('ma_3', MovingAverage(window_size=3), ['time_series']),
                 ('cal', cal, ['time_series'])]).fit_transform(X)
        
            # Train/test split
            X_train, y_train, X_test, y_test = model_selection.FeatureSplitter().transform(X_f, y)
        
            # Try sklearn's MultiOutputRegressor as time-series forecasting model
            gar = forecasting.GAR(LinearRegression())
            gar.fit(X_train, y_train).predict(X_test)
        
        Changelog
        ---------
        
        See the `RELEASE.rst <https://github.com/giotto-ai/giotto-time/blob/master/RELEASE.rst>`__ file
        for a history of notable changes to giotto-time.
        
        Contributing
        ------------
        
        We welcome new contributors of all experience levels. The Giotto
        community goals are to be helpful, welcoming, and effective. To learn more about
        making a contribution to giotto-time, please see the `CONTRIBUTING.rst
        <https://github.com/giotto-ai/giotto-time/blob/master/CONTRIBUTING.rst>`_ file.
        
        Links
        -----
        
        - Official source code repo: https://github.com/giotto-ai/giotto-time
        - Download releases: https://pypi.org/project/giotto-time/
        - Issue tracker: https://github.com/giotto-ai/giotto-time/issues
        
        Community
        ---------
        
        Giotto Slack workspace: https://slack.giotto.ai/
        
        Contacts
        --------
        
        maintainers@giotto.ai
        
Keywords: machine learning time series data analysis topology,persistence diagrams
Platform: UNKNOWN
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/x-rst
Provides-Extra: tests
Provides-Extra: doc
Provides-Extra: examples
