High Dimensional Time Series

High-dimensional time series analysis focuses on understanding and modeling complex systems generating massive amounts of temporal data across numerous variables. Current research emphasizes developing accurate and interpretable forecasting models, often employing graph neural networks, Gaussian processes, and deep learning architectures like LSTMs and transformers, to capture intricate spatiotemporal dependencies and non-linear dynamics. These advancements are crucial for improving predictions in diverse fields, including neuroscience, finance, and environmental monitoring, where accurate forecasting and anomaly detection are critical for informed decision-making and risk mitigation. Furthermore, significant effort is dedicated to developing methods that handle missing data and provide uncertainty quantification for improved reliability.