Multi-attention network with redundant information filtering for multi-horizon in multivariate time series

Multi-horizon forecasting of multivariate time series has always been a prominent research topic in domains such as finance and transportation. While prediction models that integrate attention mechanisms and Seq2Seq frameworks effectively capture the temporal dependencies between sequences, there might exist distinctions in the contributions of different input variables to the predictions. RNN-based encoders often overlook these distinctions. Hence, we propose a Multi-attention Network with Redundant Information Filtering (MNRIF) for Multi-horizon Forecasting in Multivariate Time Series, comprising the underlying component Two-stage Feature Fusion Network (TSFFN) and the upper-level structure Temporal Attention Network(TAN). TSFFN first designs a variable-level attention to screen the spatial semantics of explanatory variables during multi-step recursive iterations, effectively reducing noisy information, then builds a feature gate fusion module to adaptively weigh the degree of association between prediction targets and spatial semantics as well as target variables, thereby accomplishing secondary filtering of redundant information. TAN is a task-oriented learner that combines the Encoder-Decoder framework with attention mechanism. By means of dual information filtering based on attention mechanism and recurrent gate, it enhances effective mining of long-term temporal dependency features in sequences. Extensive experiments conducted on three real-world benchmark datasets demonstrate MNRIF's superior performance compared to various state-of-the-art methods of different types.