Anomaly Detection Paradigm for Multivariate Time Series Data Mining for Healthcare
Razaque A. Abenova M. Alotaibi M. Alotaibi B. Alshammari H. Hariri S. Alotaibi A.
September 2022MDPI
Applied Sciences (Switzerland)
2022#12Issue 17
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 an anomaly detection paradigm called novel matrix profile (NMP) to solve the all-pairs similarity search problem for time series data in the healthcare. The proposed paradigm inherits the features from two state-of-the-art algorithms: Scalable Time series Anytime Matrix Profile (STAMP) and Scalable Time-series Ordered-search Matrix Profile (STOMP). The proposed NMP caches the output in an easy-to-access fashion for single- and multidimensional data. The proposed NMP can be used on large multivariate data sets and generates approximate solutions of high quality in a reasonable time. 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.
anomalies , clustering , data mining , NMP , similarities in time series , time series
Text of the article Перейти на текст статьи
Department of Cyber Security, International Information Technology University, Almaty, 050000, Kazakhstan
Dahaa Research Group, Department of Computer Science, Shaqra University, Shaqra, 11961, Saudi Arabia
Sensor Networks and Cellular Systems (SNCS) Research Center, University of Tabuk, Tabuk, 47731, Saudi Arabia
Department of Information Technology, University of Tabuk, Tabuk, 47731, Saudi Arabia
Computer and Information Science College, Jouf University, Sakakah, 72388, Saudi Arabia
Department of Electical and Computer Engineering, University of Arizona, Tucson, 85721, AZ, United States
Computers and Information Technology College, Taif University, Taif, 21974, Saudi Arabia
Department of Cyber Security
Dahaa Research Group
Sensor Networks and Cellular Systems (SNCS) Research Center
Department of Information Technology
Computer and Information Science College
Department of Electical and Computer Engineering
Computers and Information Technology College
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026