Morphological Similarity Clustering and Its Applications in Anomaly Detection of Time Series

Abstract

Time series data clustering is an important branch and difficult topic in the field of data clustering. In this paper, the definition of temporal data morphological similarity is proposed, a set of affine invariant morphological similarity measurement methods of time series data is established, and a morphological clustering algorithm based on morphological similarity measurement is developed. Using morphological similarity measurement of time series data, two groups of abnormal change detection algorithms for time series data are established, which can be used to detect the morphological consistency of different periodical sampling series in the same time series and the morphological consistency among several time series in the same period. Based on these algorithms stated above, the multiple monitoring algorithms are proposed, which can be used to monitor states of many kinds of industry process. The effectiveness of the methods and algorithms is verified with theoretical deduction and simulation results. Simulation results show that these algorithms are very valuable for mining, clustering, modeling, statistical learning of multi-source time series data, as well as the detection and diagnosis of abnormal process changes.