MEAI-Net: Multiview embedding and attention interaction for multivariate time series forecasting

Chunru Dong, Wenqing Xu, Feng Zhang, Qiang Hua, Yong Zhang

Abstract: Multivariate time series (MTS) forecasting plays a critical role in diverse societal applications, including stock market analysis and climate change research. While many existing deep learning models have been proven to be effective in MTS forecasting through complex neural network structures and self-attention mechanisms, several challenges remain: (1) insufficient modeling of complex temporal dependencies, (2) limited ability to handle information redundancy and noise, and (3) inadequate capture of periodic characteristics of time series. To address these problems, we propose a Multiview time-dependent feature Embedding and downsampled subsequences Attention Interaction Network (MEAI-Net) for MTS forecasting. First, MEAI-Net adopts a multiview time-dependent feature embedding mechanism to extract various temporal dependency features from the sequences. Second, it reduces redundancy in the temporal sequence features through downsampling. Third, a subsequences cross-attention module is introduced to enhance information exchange between subsequences. Furthermore, we propose the period consistency loss designed to more effectively capture periodic patterns in time series data. Comprehensive experiments conducted on 12 widely used time series datasets demonstrate that MEAI-Net displays promising performance, providing a competitive alternative to current state-of-the-art approaches in MTS forecasting.

External IDs:doi:10.1016/j.neucom.2025.129769