Comparative Performance of TSDiff Self-Guidance Versus Task-Specific Conditional Diffusion Models on Probabilistic Forecasting

Abstract

Abstract: Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-confident inferences and decisions that are more risky than one thinks they are. Bayesian model averaging (BMA)provides a coherent mechanism for accounting for this model uncertainty. Several methods for implementing BMA have recently emerged. We discuss these methods and present a number of examples.In these examples, BMA provides impr

Research Question

How does the performance of TSDiff's self-guidance approach compare to task-specific conditional diffusion models on standard probabilistic forecasting benchmarks like UCR/UCI or Monash time series datasets when measured by CRPS or log-likelihood metrics?

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