Welcome to the pyWATTS documentation!
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The goals of pyWATTS (Python Workflow Automation Tool for Time-Series) are

* to support researchers in conducting automated time series experiments independent of the execution environment and
* to make methods developed during the research easily reusable for other researchers.

Therefore, pyWATTS is an automation tool for time series analysis that implements three core ideas:

* pyWATTS provides a pipeline to support the execution of experiments. This way, the execution of simple and often recurring tasks is simplified. For example, a defined preprocessing pipeline could be reused in other experiments. Furthermore, the execution of defined pipelines is independent of the execution environment. Consequently, for the repetition or reuse of a third-party experiment or pipeline, it should be sufficient to install pyWATTS and clone the third-party repository.
* pyWATTS allows to define end-to-end pipelines for experiments. Therefore, experiments can be easily executed that comprise the preprocessing, models and benchmark training, evaluation, and comparison of the models with the benchmark.
* pyWATTS defines an API that forces the different methods (called modules) to have the same interface in order to make newly developed methods more reusable.

Features of pyWATTS:

* Reuseable modules
* Plug-and-play architecture to insert modules into the pipeline
* End-to-end pipeline for experiments such that pipeline performs all necessary steps from preprocessing to evaluation
* Conditions within the pipeline
* Saving and loading of the entire pipeline including the pipeline modules
* Adapters and wrappers for existing machine learning libraries


Table of Content
----------------

.. toctree::
   :maxdepth: 2

   install
   getting_started
   how_to_use
   core
   advanced_example
   neural_network_example
   about_us
   contribution


   
pyWATTS API
===========

.. toctree::
   :maxdepth: 5

   api/pywatts



Indices and tables
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* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`