Welcome to the pyWATTS documentation!¶

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¶