Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting

arXiv:2605.23402v1 Announce Type: new Abstract: Effectively modeling non-stationary dynamics in probabilistic multivariate time series(MTS) forecasting requires balancing expressiveness with robustness. Existing parametric approaches benefit from strong inductive biases but lack flexibility, whereas deep generative models struggle to capture complex temporal dependencies without extensive data and computation. We introduce Parametric Prior Mapping (PPM), a framework that injects parametric structural priors into a generative modeling process. Specifically, PPM utilizes a parametric estimator t

Source: arXiv cs.LG — read the full report at the original publisher.

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