TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting

Peiyuan Liu, Beiliang Wu, Yifan Hu, Naiqi Li, Tao Dai, Jigang Bao, Shu-Tao Xia

Keywords: Long-term time series forecasting, Non-stationarity, Dependency Modeling

TL;DR: TimeBridge is a novel framework designed to bridge the gap between non-stationarity and dependency modeling in long-term time series forecasting.

Abstract: 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. The code is available in the supplementary material.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 3735