提出DTAF框架，同时处理时间与频域非平稳性，提升长时序预测精度。
Towards Non-Stationary Time Series Forecasting with Temporal Stabilization and Frequency Differencing
- 双分支架构：时域用TFS模块分离非平稳模式，频域用FWM模块动态突出谱变成分。
- 在真实数据集上显著优于现有基线，尤其在长期预测中表现更稳健。
- 适合能源、金融等存在剧烈变化的动态系统预测任务。
时序预测在能源、金融、交通和云计算等动态领域至关重要。然而，现实时序常呈现非平稳性，包括时间分布偏移和频谱变化，给长期预测带来挑战。本文提出DTAF——一种双分支框架，分别从时域与频域应对非平稳性。时域方面，时序稳定融合（TFS）模块采用非平稳专家混合（MOE）滤波器，解耦并抑制时间非平稳模式，同时保留长期依赖；频域方面，频波建模（FWM）模块引入频域差分，动态强调具有显著谱变的成分。通过融合TFS与FWM的互补输出，DTAF生成适应双重非平稳性的鲁棒预测。在多个真实世界基准上的大量实验表明，该方法显著优于当前先进基线，在非平稳条件下实现更高预测精度。代码已公开于https://github.com/decisionintelligence/DTAF。
原文摘要 · Abstract (English)
Time series forecasting is critical for decision-making across dynamic domains such as energy, finance, transportation, and cloud computing. However, real-world time series often exhibit non-stationarity, including temporal distribution shifts and spectral variability, which pose significant challenges for long-term time series forecasting. In this paper, we propose DTAF, a dual-branch framework that addresses non-stationarity in both the temporal and frequency domains. For the temporal domain, the Temporal Stabilizing Fusion (TFS) module employs a non-stationary mix of experts (MOE) filter to disentangle and suppress temporal non-stationary patterns while preserving long-term dependencies. For the frequency domain, the Frequency Wave Modeling (FWM) module applies frequency differencing to dynamically highlight components with significant spectral shifts. By fusing the complementary outputs of TFS and FWM, DTAF generates robust forecasts that adapt to both temporal and frequency domain non-stationarity. Extensive experiments on real-world benchmarks demonstrate that DTAF outperforms state-of-the-art baselines, yielding significant improvements in forecasting accuracy under non-stationary conditions. All codes are available at https://github.com/decisionintelligence/DTAF.
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