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arXiv Machine Learning By Weiwei Ye, Dongyuan Li, Hangchen Liu, Haotong Jiang, Yoshihide Sekimoto, Renhe Jiang

DiffPTS: Rethinking Diffusion ELBO for Probabilistic Time Series Forecasting

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DiffPTS rethinks the evidence lower bound (ELBO) for diffusion-based probabilistic time‑series forecasting by adopting a Location‑Scale Noise Model, which yields a Gaussian negative log‑likelihood objective and a unified joint training objective. The framework enables end‑to‑end optimization of all components within the ELBO, unlike prior methods that treat estimator training as separate regression tasks. On multiple benchmarks, DiffPTS achieves state‑of‑the‑art performance, reducing CRPS/MSE by over 14.53%/16.55% compared to existing diffusion‑based models.

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