Multivariate Time Series Forecasting Based on Multi-Scale Feature Fusion and Dual-Attention Mechanism

Abstract

Each subsequence of the Multivariate Time Series(MTS) contains multi-scale characteristics of different time spans, comprising information such as development process, direction, and trend. However, existing time series prediction models cannot effectively capture multi-scale features and evaluate their importance. In this study, a MTS prediction network, FFANet, is proposed based on multi-scale temporal feature fusion and a Dual-Attention Mechanism(DAM).FFANet effectively integrates multi-scale features and focuses on important parts.Utilizing the parallel temporal dilation convolution layer in the multi-scale temporal feature fusion module endows the model with multiple receptive domains to extract features of temporal data at different scales and adaptively fuse them based on their importance. Using a DAM to recalibrate the fused temporal features, FFANet focuses on features that make significant contributions to prediction by assigning temporal and channel attention weights and weighting them to the corresponding temporal features. The experimental results show that compared with AR, VARMLP, RNN-GRU, LSTNet-skip, TPA-LSTM, MTGNN, and AttnAR time series prediction models, FFANet achieves average reduction of 0.152 3、0.120 0、0.074 3、0.035 4、0.021 5、0.012 1、0.020 0 in RRSE prediction error on Traffic, Solar Energy, and Electricity datasets, respectively.