VLinear: Enhanced linear complexity time series forecasting model

This study presents a novel time series prediction model(VLinear), designed with linear computational complexity, demonstrating superior predictive performance compared to DLiner without increasing parameter count or computational demands. Our model introduces two key innovations: first, a Feature Pyramid Network (FPN) is employed to effectively capture time series data characteristics, bypassing the traditional decomposition into trend and seasonal components. Second, a multi-layer fusion structure is developed to integrate deep and shallow features seamlessly. Empirically, VLinear outperforms DLiner in 31 out of 32 test cases on eight open-source datasets, with an average reduction of 16.8% in mean squared error (MSE) and 11.8% in mean absolute error (MAE). Additionally, compared to the transformer-based PatchTST, VLinear achieves 10 best MSE and 15 best MAE results, using only 8% of PatchTST’s total computational load in the 32 test projects.