Multivariate Time Series

Multivariate time series analysis focuses on understanding and predicting the interconnected behavior of multiple time-dependent variables. Current research emphasizes developing advanced deep learning models, such as Transformers, state-space models (like Mamba and its variants), and convolutional neural networks, to effectively capture complex temporal and cross-variable dependencies, including lead-lag dynamics and non-stationary patterns. These advancements are crucial for improving forecasting accuracy in diverse applications, from environmental monitoring (e.g., smog prediction) to healthcare (e.g., anomaly detection in medical time series) and telecommunications. Furthermore, significant effort is dedicated to enhancing model robustness against attacks and improving interpretability through techniques like influence functions and counterfactual explanations.