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.
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
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.