自适应时间序列预测新模型，通过频谱引导降噪提升恢复精度
StaTS: Spectral Trajectory Schedule Learning for Adaptive Time Series Forecasting with Frequency Guided Denoiser
- 学习数据自适应的噪声调度与频谱引导的去噪器
- 在多个真实数据集上减少采样步数仍保持高精度
- 适合需要高效精准时间序列预测的研究者
扩散模型在概率性时间序列预测中展现巨大潜力，但固定噪声调度常导致中间状态难以逆推，终端状态偏离近似无噪假设。现有方法多依赖时域条件，忽视调度引发的频谱退化，限制了跨噪声水平的结构恢复。本文提出StaTS，一种通过交替优化学习噪声调度与去噪器的扩散模型。其包含谱轨迹调度器（STS），通过频谱正则化学习数据自适应的噪声调度，增强结构保持与逐步可逆性；以及频谱引导去噪器（FGD），估计调度引起的频谱失真，并据此调节各扩散步骤和变量的去噪强度，实现异质恢复。采用两阶段训练稳定调度学习与去噪优化的耦合。多个真实世界基准测试显示一致性能提升，同时在更少采样步数下保持强表现。代码已公开于https://github.com/zjt-gpu/StaTS/。
原文摘要 · Abstract (English)
Diffusion models have been used for probabilistic time series forecasting and show strong potential. However, fixed noise schedules often produce intermediate states that are hard to invert and a terminal state that deviates from the near noise assumption. Meanwhile, prior methods rely on time domain conditioning and seldom model schedule induced spectral degradation, which limits structure recovery across noise levels. We propose StaTS, a diffusion model for probabilistic time series forecasting that learns the noise schedule and the denoiser through alternating updates. StaTS includes Spectral Trajectory Scheduler (STS) that learns a data adaptive noise schedule with spectral regularization to improve structural preservation and stepwise invertibility, and Frequency Guided Denoiser (FGD) that estimates schedule induced spectral distortion and uses it to modulate denoising strength for heterogeneous restoration across diffusion steps and variables. A two stage training procedure stabilizes the coupling between schedule learning and denoiser optimization. Experiments on multiple real world benchmarks show consistent gains, while maintaining strong performance with fewer sampling steps. Our code is available at https://github.com/zjt-gpu/StaTS/.
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