FPN-fusion: Enhanced Linear Complexity Time Series Forecasting Model
Abstract
This study presents a novel time series prediction model, FPN-fusion, 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-level fusion structure is developed to integrate deep and shallow features seamlessly. Empirically, FPN-fusion 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, FPN-fusion achieves 10 best MSE and 15 best MAE results, using only 8% of PatchTST's total computational load in the 32 test projects.
Cite
@article{arxiv.2406.06603,
title = {FPN-fusion: Enhanced Linear Complexity Time Series Forecasting Model},
author = {Chu Li and Pingjia Xiao and Qiping Yuan},
journal= {arXiv preprint arXiv:2406.06603},
year = {2024}
}
Comments
FPN,time series,fusion. arXiv admin note: text overlap with arXiv:2401.03001 by other authors