LW-MSTCNN: An Optimization Study on Integrating Multiscale Attention Mechanism with Deep Separable Convolutional Networks

Multivariate time series (MTS) forecasting is crucial in the finance, energy and transportation sectors. Although current models deliver high prediction accuracy, their complexity and extensive computational demands hinder practical deployment. To address these limitations, this paper introduces a lightweight multiscale spatio-temporal convolutional network (LW-MSTCNN) that leverages deep separable convolution (DSCNN) to build an efficient multiscale pyramid model to decrease model complexity and computational costs. Additionally, a multiscale attention (MA) mechanism is incorporated to selectively emphasize important features across different spatial and temporal scales, improving feature extraction and overall performance. This combined approach enhances the model’s ability to process high-dimensional data efficiently while capturing critical multiscale patterns, addressing key challenges in spatio-temporal data processing. Experimental results on four datasets demonstrate that the proposed model significantly reduces the usage of computational resources, with the parameter count reduced by 4 to 5 times, while maintaining comparable prediction accuracy. Additionally, extensive generalization experiments show that the model exhibits strong robustness. This highlights the effectiveness and practicality of the model in multivariate time series (MTS) forecasting.