CMDA: a tool for Continuous Monitoring Data Analysis

arXiv (Cornell University)

Abstract

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.

Authors 2

1. Pejman Farhadi Ghalati Aachen Joint Research Centre for Computational Biomedicine (JRC-COMBINE)

   RWTH Aachen University

   Affiliation as printed

   Joint Research Center for Computational Biomedicine RWTH Aachen University Aachen , Germany

2. Andreas Schuppert Aachen Joint Research Centre for Computational Biomedicine (JRC-COMBINE)

   RWTH Aachen University

   Affiliation as printed

   Joint Research Center for Computational Biomedicine RWTH Aachen University Aachen , Germany