Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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

How to cite: Razaque, A.; Abenova, M.; Alotaibi, M.; Alotaibi, B.; Alshammari, H.; Hariri, S.; Alotaibi, A. Hybrid Algorithm for Anomaly Removal in Time Series Data Mining. Preprints 2021, 2021110440 (doi: 10.20944/preprints202111.0440.v1). Razaque, A.; Abenova, M.; Alotaibi, M.; Alotaibi, B.; Alshammari, H.; Hariri, S.; Alotaibi, A. Hybrid Algorithm for Anomaly Removal in Time Series Data Mining. Preprints 2021, 2021110440 (doi: 10.20944/preprints202111.0440.v1).

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 & Systems Engineering

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