Quantile-Guided Diffusion for Probabilistic Time-Series Forecasting

Key points

• Quantile forecasts summarize uncertainty at each future time step, while diffusion forecasters represent it through sampled future trajectories.1
• Specifically, we investigate how the mechanism of quantile integration affects its effectiveness in guiding a pretrained diffusion forecaster.1
• We find that residual control throughout the denoising network yields the most consistent improvements.1
• Finally, we introduce the Conditional Linear Predictive Score (CLPS), which complements distributional metrics by evaluating the downstream forecasting utility of generated futures.1

Sources (1)

• [1]Quantile-Guided Diffusion for Probabilistic Time-Series Forecasting
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 06:01 PM

  We study how predicted quantiles can serve as distributional controls for diffusion forecasting.

  Quantile forecasts summarize uncertainty at each future time step, while diffusion forecasters represent it through sampled future trajectories.

Extractive summary: sentences quoted from the sources.