Title: Adaptive Graph Convolutional Network with Attention Fusion for Multivariate Time Series Forecasting with Variable Missing
Primary area: learning on time series and dynamical systems
Keywords: MTSF+Missing Variable-Subset+Spatiotemporal Dependency+Dynamic Graph Convolution

Abstract: Multivariate time series forecasting (MTSF) plays a vital role in diverse applications such as traffic prediction, weather research, and energy management. However, missing subset variable forecastinh has emerged as a critical challenge in MTSF due to factors such as sensor failures and maintenance. Variable incompleteness severely hinders the ability of forecasting models to capture intrinsic inter-variable relationships. Existing approaches either suffer from severe error accumulation, lack flexible mechanisms for handling missing data, or overly rely on local spatiotemporal correlations. To address these limitations, we propose VMPredictor, a novel end-to-end framework that effectively models spatiotemporal dependencies among incomplete variables for accurate forecasting. VMPredictor incorporates two key components: (1) the Adaptive Missing Filling and Enhancement Layer , which introduces learnable embeddings to adaptively fill missing positions and dynamically refine incomplete representations during training; and (2) the Spatiotemporal Dependency Mining Layer, built upon a Dynamic Graph Convolution Layer-Normalized Gated Recurrent Unit, where dynamic graph convolution adaptively reconstructs spatial correlations and replaces all fully connected layers in GRU to capture synchronized spatiotemporal dependencies. Together, these innovations endow VMPredictor with robust missing-data handling and precise spatiotemporal relation modeling. Extensive experiments on five real-world datasets under varying missing rates demonstrate the superiority of our approach over existing baselines. Codes can be found at https://anonymous.4open.science/r/Code-A216/.