Time Series Similarity

Time series similarity research focuses on efficiently identifying and quantifying the resemblance between different time series datasets, enabling pattern discovery and improved forecasting. Current efforts concentrate on developing scalable algorithms, such as those leveraging graph neural networks and optimized similarity measures like Dynamic Time Warping (DTW) variants, to handle high-dimensional and multivariate data, often incorporating contextual information from related series. These advancements are crucial for diverse applications, including anomaly detection, fault localization in complex systems (like networks), and improving the accuracy of time series forecasting models by incorporating relevant exogenous data.