Time Series Segmentation

Time series segmentation aims to automatically partition continuous data streams into meaningful segments reflecting underlying changes in the process being monitored. Current research emphasizes developing efficient and accurate algorithms, focusing on both univariate and multivariate time series, with approaches ranging from statistical methods and graph-based models to deep learning architectures like LSTM-autoencoders and sequence-to-sequence networks. These advancements improve the analysis of diverse data sources, from industrial sensor readings to physiological signals, enabling more precise event detection and improved decision-making in various applications. The ultimate goal is to create robust, parameter-free, and scalable methods applicable across domains.