tsflex: flexible time series processing & feature extraction

24 Nov 2021  ·  Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost, Sofie Van Hoecke ·

Time series processing and feature extraction are crucial and time-intensive steps in conventional machine learning pipelines. Existing packages are limited in their real-world applicability, as they cannot cope with irregularly-sampled and asynchronous data... We therefore present $\texttt{tsflex}$, a domain-independent, flexible, and sequence first Python toolkit for processing & feature extraction, that is capable of handling irregularly-sampled sequences with unaligned measurements. This toolkit is sequence first as (1) sequence based arguments are leveraged for strided-window feature extraction, and (2) the sequence-index is maintained through all supported operations. $\texttt{tsflex}$ is flexible as it natively 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 rate regularity and synchronization. Other functionalities from this package are multiprocessing, in-depth execution time logging, support for categorical & time based data, chunking sequences, and embedded serialization. $\texttt{tsflex}$ is developed to enable fast and memory-efficient time series processing & feature extraction. Results indicate that $\texttt{tsflex}$ is more flexible than similar packages while outperforming these toolkits in both runtime and memory usage. read more

PDF Abstract


  Add Datasets introduced or used in this paper

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here