Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Hybrid Algorithm for Anomaly Removal in Time Series Data Mining

Version 1 : Received: 20 November 2021 / Approved: 23 November 2021 / Online: 23 November 2021 (17:51:42 CET)

A peer-reviewed article of this Preprint also exists.

Razaque, A.; Abenova, M.; Alotaibi, M.; Alotaibi, B.; Alshammari, H.; Hariri, S.; Alotaibi, A. Anomaly Detection Paradigm for Multivariate Time Series Data Mining for Healthcare. Appl. Sci. 2022, 12, 8902. Razaque, A.; Abenova, M.; Alotaibi, M.; Alotaibi, B.; Alshammari, H.; Hariri, S.; Alotaibi, A. Anomaly Detection Paradigm for Multivariate Time Series Data Mining for Healthcare. Appl. Sci. 2022, 12, 8902.

Abstract

Time series data are significant and are derived from temporal data, which involve real numbers representing values collected regularly over time. Time series have a great impact on many types of data. However, time series have anomalies. We introduce hybrid algorithm named novel matrix profile (NMP) to solve the all-pairs similarity search problem for time series data. The proposed NMP inherits the features from two state-of-the art algorithms: similarity time-series automatic multivariate prediction (STAMP), and short text online microblogging protocol (STOMP). The proposed algorithm caches the output in an easy-to-access fashion for single- and multidimensional data. The proposed NMP algorithm can be used on large data sets and generates approximate solutions of high quality in a reasonable time. The proposed NMP can also handle several data mining tasks. It is implemented on a Python platform. To determine its effectiveness, it is compared with the state-of-the-art matrix profile algorithms i.e., STAMP and STOMP. The results confirm that the proposed NMP provides higher accuracy than the compared algorithms.

Keywords

time series; NMP algorithm; anomalies; data mining; similarities in time series; clustering

Subject

Engineering, Control and Systems Engineering

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