Periodicity Variations Modelling Based on <scp>2D</scp> Multi‐Scale Patch for Multivariate Time Series Forecasting Using Improved <scp>MLP</scp> and Depthwise Separable Convolution

ABSTRACT Multivariate time series forecasting (MTSF) involves predicting future values of multiple interrelated variables based on historical observations. While existing models often struggle to capture complex temporal multi‐scale dependencies and simultaneously modelling intraperiod and interperiod variations, thereby limiting their predictive accuracy. To address these limitations, we introduce a novel forecasting model named MTSPnet. This model employs a two‐dimensional (2D) temporal multi‐scale patching strategy, which converts one‐dimensional (1D) time series data into 2D multi‐scale Patch across different time periods. Additionally, MTSPnet incorporates two complementary modules: an interactive multilayer perceptron (MLPmix) module and a dynamic depthwise separable convolution (DDSC) module. These modules enable MTSPnet to efficiently extract both local and global temporal features, further enhancing its ability to model multi‐scale dependencies and periodicity variations. Experimental evaluations on seven real‐world datasets demonstrate that MTSPnet achieves superior performance in long‐term forecasting, proving its effectiveness as a robust and efficient solution for accurate time series prediction.