Multivariate Time Series Forecasting

Multivariate time series forecasting aims to predict the future values of multiple interconnected time series, a crucial task across diverse fields like finance and healthcare. Current research emphasizes improving model accuracy and robustness, focusing on architectures like Transformers and Graph Neural Networks, as well as addressing challenges such as non-stationarity, missing data, and computational efficiency through techniques like frequency domain analysis and lightweight model designs. These advancements are significant for improving decision-making in various applications by providing more accurate and reliable predictions, particularly in scenarios with high-dimensional data and complex temporal dependencies.