Utilizing an adaptive window rolling median methodology for time series anomaly detection

• Published in: Procedia computer science Vol. 217; pp. 584 - 593
• Main Authors: Dimoudis, Dimitris, Vafeiadis, Thanasis, Nizamis, Alexandros, Ioannidis, Dimosthenis, Tzovaras, Dimitrios
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2023
• Subjects: adaptive window; Anomaly detection; Numenta's NAB; rolling median; Yahoo's Webscope S5; adaptive window; rolling median; Numenta's NAB; Anomaly detection; Yahoo's Webscope S5
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2022.12.254

With the rise of industry 4.0 and the amount of data gathered from sensors, anomaly detection has become an extremely important task. Due to the variety of anomalies, a universal approach is not yet possible, therefore, many methods to identify abnormal behaviours on data have been researched. In this paper, a new anomaly detection algorithm is proposed that spots abnormal data points in time series using a rolling median with an adaptive sliding window. The window changes based on two methods, F1 based and T-test. F1 method tries to make the F1 score have only an upward trend, while T-test recognizes trends in time series and adjusts the window accordingly. For the evaluation, two well - known benchmark datasets were employed. Moreover, the proposed algorithm was also tested on a dataset consisting of real industrial machinery sensor observations coming from a furniture manufacturer. In the two benchmark datasets mentioned, the two variants are compared with an ensemble of 7 models. The results indicate that the proposed method achieves, in the most cases, better F1 score compared to the benchmarks.