Over the last few years, with the growth of time-series collecting and storing, there has been a great demand for tools and software for temporal data engineering and modeling. This paper presents a generic workflow for time series data research, including temporal data importing, preprocessing, and feature extraction. This framework is developed and built as a robust and easy-to-use Python package, called CMDA, with a modular structure that offers tools to prepare raw data, allowing both scientists and non-experts to analyze various temporal data structures.
@article{arxiv.2310.14427,
title = {CMDA: a tool for Continuous Monitoring Data Analysis},
author = {Pejman Farhadi Ghalati and Andreas Schuppert},
journal= {arXiv preprint arXiv:2310.14427},
year = {2023}
}