Ji-Hyun Choi, Hyun Ahn, Journal of Internet Computing and Services, Vol. 25, No. 2, pp. 39-47, Apr. 2024
10.7472/jksii.2024.25.2.39, Full Text:  HTML
Keywords: Time-series Similarity, Anomaly Detection, Subsequences, Spearman’s Rank Correlation Coefficient

Abstract

Time series analysis is widely employed by many organizations to solve business problems, as it extracts various information and insights from chronologically ordered data. Among its applications, measuring time series similarity is a step to identify time series with similar patterns, which is very important in time series analysis applications such as time series search and clustering. In this study, we propose an efficient method for measuring time series similarity that focuses on anomalies rather than the entire series. In this regard, we validate the proposed method by measuring and analyzing the rank correlation between the similarity measure for the set of subsets extracted by anomaly detection and the similarity measure for the whole time series. Experimental results, especially with stock time series data and an anomaly proportion of 10%, demonstrate a Spearman’s rank correlation coefficient of up to 0.9. In conclusion, the proposed method can significantly reduce computation cost of measuring time series similarity, while providing reliable time series search and clustering results.

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