Pattern-based Time Series Semantic Segmentation with Gradual State Transitions
Louis Carpentier, Len Feremans, Wannes Meert, Mathias Verbeke · Society for Industrial and Applied Mathematics eBooks · 2024
Time series semantic segmentation is the task of extracting time intervals from the time series data that share a similar meaning within the application domain in an unsupervised manner. State-of-the-art algorithms typically treat this problem as change point detection, resulting in discrete state transitions. However, in real-world applications, states often transition gradually. This leads to a novel, more challenging variation of the traditional time series segmentation task, for which we present PaTSS, a novel, domain-agnostic algorithm to uncover those gradual state transitions. PaTSS learns a distribution over the semantic segments based on an embedding space derived from mined sequential patterns. An extensive experimental evaluation on 107 benchmark time series shows that PaTSS is capable of detecting gradual state transitions, a task current methods are unable to perform.