TS-PCA 0.0.1

A package for unsupervised representation and principal component analysis of irregularly sampled time series with variable size relying on the shape analysis literature.

PCA for time series

Authors: Samuel Gruffaz, Thibaut Germain

This repository gathers the functions developed in the paper “Shape Analysis for Time Series”, located in the src directory.

It is possible to represent irregularly sampled time series of different lengths and to apply kernel PCA to these representations in order to identify the main modes of shape variation in the time series.

These methods work particularly well when the analyzed dataset is homogeneous in terms of shapes, for example when each time series corresponds to:

• a heartbeat recording,
• a respiratory cycle,
• an electricity consumption pattern,
• or a heating load curve.

The Docs directory contains the files used to build the package documentation.

The pages directory contains the pages used to launch a Streamlit application from the menu, allowing users to test the different building blocks of the code.

Coming next:

• A class that combines the essential functions to simplify the user experience
• Complete documentation
• A PyPI release
• New kernels