Time series anomaly detection has received substantial attention over the past two decades, leading to the development of hundreds of algorithms. However, comprehensively understanding this vast landscape remains challenging, particularly for non-experts and novices. In this demonstration paper, we present InTimeAD, an interactive web application that provides access to more than 30 state-of-the-art time series anomaly detection algorithms. InTimeAD is intended to explore the performance of existing as well as custom anomaly detection models in an interactive, hands-on manner. By lowering the entry bar, we support practitioners overwhelmed by the large number of existing techniques, while providing a platform for researchers to rapidly analyze their novel anomaly detection algorithms.

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.