Getting started easily with the time series analysis(TSA)

State of the art algorithms(TSC) in 2020

Solid bars indicate cliques, within which there is no significant difference in rank. Tests are performed with the sign rank test using the Holm correction. Top clique of four classifiers represent the state of the art in Spring 2020.

Click here to get more details about this ranking.

Datasets

• UEA & UCR Time Series Classification Repository is an ongoing project to develop a comprehensive repository for research into time series classification.
• Peter H Charlton's Project (clinical signals) composes of four clinical time series (Respiratory rate, pulse wave, etc.) now. Besides, he also provided some useful toolboxes for time series mentioned above.

Python's libs

• The objective of the pyts Python package is to make time series classification easily accessible by providing preprocessing and utility tools, and implementations (Comparisons of performance) of several algorithms for time series classification.
• tslearn (sklearn flavour) is a Python package that provides machine learning tools for the analysis of time series. This package builds on (and hence depends on) scikit-learn, numpy and scipy libraries.