用可学习的先验结构提升非平稳时间序列预测的精度与效率
Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting
- 引入参数化先验映射框架，动态生成适应数据的先验分布
- 在多个非平稳数据集上超越基线模型，不确定性估计更准确
- 适合需要高效高精度预测的工业级时序任务
在概率多变量时间序列预测中，有效建模非平稳动态需兼顾表达能力与鲁棒性。现有参数化方法虽具强归纳偏置但灵活性不足，深度生成模型则需大量数据与计算才能捕捉复杂时序依赖。本文提出参数化先验映射（PPM）框架，将参数化结构先验注入生成建模过程。具体地，通过参数化估计器生成动态自适应先验，再经可学习映射引导复杂预测分布的学习。该设计使模型兼具参数方法的高效性与生成模型的表达力。采用混合目标训练后，PPM在多个非平稳数据集上实现更精确的预测和校准良好的不确定性估计，展现出更高的准确性与计算效率平衡。代码已开源。
原文摘要 · Abstract (English)
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 to derive a dynamic, adaptive prior that guides the learning of a complex predictive distribution via a learnable mapping. This design allows the model to retain the efficiency of parametric methods while exploiting the expressive power of generative models. Trained with a hybrid objective, PPM yields precise forecasts with well-calibrated uncertainty estimates. Empirical results show that PPM outperforms existing baselines in handling non-stationary data, offering a superior trade-off between accuracy and computational efficiency. The code is available at https://github.com/ljl8336/PPM.
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