tsflex: flexible time series processing & feature extraction

TL;DR

tsflex is a Python toolkit that offers flexible, efficient, and broad applicability for time series processing and feature extraction, especially for irregularly-sampled and asynchronous data.

Contribution

It introduces a highly flexible and efficient toolkit that supports multivariate, irregular, and asynchronous time series processing with integration capabilities.

Findings

tsflex outperforms similar packages in speed and memory efficiency

Supports irregular sampling and asynchronous data without assumptions

Enables multiple window-stride configurations and integration with other functions

Abstract

Time series processing and feature extraction are crucial and time-intensive steps in conventional machine learning pipelines. Existing packages are limited in their applicability, as they cannot cope with irregularly-sampled or asynchronous data and make strong assumptions about the data format. Moreover, these packages do not focus on execution speed and memory efficiency, resulting in considerable overhead. We present $\texttt{tsflex}$, a Python toolkit for time series processing and feature extraction, that focuses on performance and flexibility, enabling broad applicability. This toolkit leverages window-stride arguments of the same data type as the sequence-index, and maintains the sequence-index through all operations. $\texttt{tsflex}$ is flexible as it supports (1) multivariate time series, (2) multiple window-stride configurations, and (3) integrates with processing and feature functions from other packages, while (4) making no assumptions about the data sampling regularity, series alignment, and data type. Other functionalities include multiprocessing, detailed execution logging, chunking sequences, and serialization. Benchmarks show that $\texttt{tsflex}$ is faster and more memory-efficient compared to similar packages, while being more permissive and flexible in its utilization.