A scalable approach to detect Contrasting Consecutive Patterns in time series data
Stefano Tata, Luca Cagliero · 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) · 2022
The Matrix Profile is an established data structure used to efficiently compute similarities among subseries in large time series datasets. State-of-the-art anomaly detection methods rely on the Matrix Profile to identify abnormal behaviors in an unsupervised fashion. Their time complexity is quadratic with the series length. This paper aims at further enhancing the scalability of Matrix Profile-based anomaly detection approaches. The key idea is to restrict the search to a particular kind of anomalies, namely the Contrasting Consecutive Patterns. They consist of two consecutive, highly dissimilar subsequences observed in the input time series data. To efficiently extract such anomalous patterns, we present a new algorithm that scales linearly with the time series length. The experimental results show the efficiency and usability of the proposed approach on real-world time series datasets.