Wuman Luo

Efficient and explainable multivariate time series forecasting: a survey of architectures, taxonomies, and open challenges

This survey re-examines deep learning models for MTS forecasting through the requirements of efficiency and explainability, and identifies key open challenges including the absence of standardized explainability benchmarks for time series, the interpretability gap in state space models, and the need to advance from correlational to causal explanations.