FPN-fusion: Enhanced Linear Complexity Time Series Forecasting Model
<|reference_start|>FPN-fusion: Enhanced Linear Complexity Time Series Forecasting Model: 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.<|reference_end|>
@article{li2024fpn-fusion:,
    title={FPN-fusion: Enhanced Linear Complexity Time Series Forecasting Model},
    author={Chu Li, Pingjia Xiao, Qiping Yuan},
    journal={arXiv preprint arXiv:2406.06603},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06603},
    primaryClass={cs.LG cs.AI}
}