Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm. (arXiv:2209.04213v1 [eess.SP])

This paper introduces an algorithm for the detection of change-points and the
identification of the corresponding subsequences in transient multivariate
time-series data (MTSD). The analysis of such data has become more and more
important due to the increase of availability in many industrial fields.
Labeling, sorting or filtering highly transient measurement data for training
condition based maintenance (CbM) models is cumbersome and error-prone. For
some applications it can be sufficient to filter measurements by simple
thresholds or finding change-points based …

algorithm arxiv autoencoder clustering clustering algorithm iterative modeling series