Revisiting Multivariate Time Series Forecasting with Missing Values

The article "Revisiting Multivariate Time Series Forecasting with Missing Values" addresses the significant challenges posed by missing data in multivariate time series forecasting. It emphasizes the critical need for developing reliable prediction methods that can effectively handle these complexities. The current standard approach, known as the imputation-then-prediction framework, is critically examined and found to be insufficient in fully addressing the intricacies of missing values. This critique highlights that simply imputing missing data before forecasting may overlook important dependencies and patterns inherent in the data. The discussion aligns with ongoing research efforts that underscore the importance of improving prediction methodologies to enhance forecasting accuracy. By revisiting these challenges, the article contributes to a broader conversation about advancing statistical learning techniques in the presence of incomplete data. This perspective is consistent with recent analyses that question existing frameworks and call for more robust solutions in time series forecasting.