[2S1-GS-2] Machine learning:

[2S1-GS-2-04] Time Series Discord Discovery with Missing Values

〇Kento Kotera¹, Akihiro Yamaguchi¹, Ken Ueno¹ (1. Toshiba Corporation)

Keywords:Time series, Anomaly detection, Subsequence, Similarity search

Time series anomaly detection remains a crucial topic in data mining. Recent studies indicate that methods based on identifying anomaly subsequences, known as time series discords, are still effective for univariate time series anomaly detection. Despite the frequent occurrence of missing data in real-world time series, there are no established methods for discovering discords in data with missing values. In this study, we propose a novel approach that efficiently identifies discord candidates by simultaneously performing a new metric that can be calculated for subsequences with missing values and a nearest-neighbor-based time series imputation. Our experimental results demonstrate that this method achieves superior anomaly detection accuracy compared to other distance-based anomaly detection approaches in the presence of missing values.