  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.
