StaTS: Spectral Trajectory Schedule Learning for Adaptive Time Series Forecasting with Frequency Guided Denoiser
arXiv:2603.00037v1 Announce Type: new Abstract: 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 an
arXiv:2603.00037v1 Announce Type: new Abstract: 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/.
Executive Summary
The article proposes StaTS, a diffusion model for probabilistic time series forecasting that learns the noise schedule and denoiser through alternating updates. StaTS includes a Spectral Trajectory Scheduler and a Frequency Guided Denoiser to improve structural preservation and stepwise invertibility. The model shows consistent gains on multiple real-world benchmarks while maintaining strong performance with fewer sampling steps.
Key Points
- ▸ StaTS learns the noise schedule and denoiser through alternating updates
- ▸ Spectral Trajectory Scheduler improves structural preservation and stepwise invertibility
- ▸ Frequency Guided Denoiser estimates schedule-induced spectral distortion for heterogeneous restoration
Merits
Improved Performance
StaTS shows consistent gains on multiple real-world benchmarks
Efficient Sampling
The model maintains strong performance with fewer sampling steps
Demerits
Complexity
The alternating update mechanism and spectral regularization may increase model complexity
Training Instability
The two-stage training procedure may require careful tuning to stabilize the coupling between schedule learning and denoiser optimization
Expert Commentary
The proposed StaTS model addresses significant limitations in existing diffusion models for time series forecasting. By learning the noise schedule and denoiser through alternating updates, StaTS improves structural preservation and stepwise invertibility. The use of spectral regularization and frequency-guided denoising also enhances the model's ability to capture complex patterns in time series data. However, the increased model complexity and potential training instability require careful consideration. Overall, StaTS is a promising approach that warrants further research and evaluation.
Recommendations
- ✓ Further evaluation of StaTS on diverse time series datasets to assess its generalizability
- ✓ Investigation of the model's potential applications in other domains, such as image or audio processing