TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting
<|reference_start|>TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting: Non-stationarity poses significant challenges for multivariate time series forecasting due to the inherent short-term fluctuations and long-term trends that can lead to spurious regressions or obscure essential long-term relationships. Most existing methods either eliminate or retain non-stationarity without adequately addressing its distinct impacts on short-term and long-term modeling. Eliminating non-stationarity is essential for avoiding spurious regressions and capturing local dependencies in short-term modeling, while preserving it is crucial for revealing long-term cointegration across variates. In this paper, we propose TimeBridge, a novel framework designed to bridge the gap between non-stationarity and dependency modeling in long-term time series forecasting. By segmenting input series into smaller patches, TimeBridge applies Integrated Attention to mitigate short-term non-stationarity and capture stable dependencies within each variate, while Cointegrated Attention preserves non-stationarity to model long-term cointegration across variates. Extensive experiments show that TimeBridge consistently achieves state-of-the-art performance in both short-term and long-term forecasting. Additionally, TimeBridge demonstrates exceptional performance in financial forecasting on the CSI 500 and S&P 500 indices, further validating its robustness and effectiveness. Code is available at \url{https://github.com/Hank0626/TimeBridge}.<|reference_end|>
@article{liu2024timebridge:,
    title={TimeBridge: Non-Stationarity Matters for Long-term Time Series
  Forecasting},
    author={Peiyuan Liu, Beiliang Wu, Yifan Hu, Naiqi Li, Tao Dai, Jigang Bao,
  Shu-tao Xia},
    journal={arXiv preprint arXiv:2410.04442},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04442},
    primaryClass={cs.LG stat.ML}
}