tsflex: flexible time series processing & feature extraction
This toolkit addresses inefficiencies in existing packages for time series analysis, benefiting data scientists and researchers working with irregular or asynchronous data.
The paper tackles the problem of time series processing and feature extraction by introducing tsflex, a Python toolkit that focuses on performance and flexibility, with benchmarks showing it is faster and more memory-efficient than similar packages.
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.