用递归网络+扩散模型，提升多变量时间序列预测的精度与效率。
Recurrent Interpolants for Probabilistic Time Series Prediction
- 基于随机插值和条件生成，融合递归网络与扩散模型优势
- 有效建模高维分布与跨特征依赖关系，提升预测概率质量
- 适合需要高精度概率预测的工业、金融场景应用
序列模型如循环神经网络和变压器已成为各领域多变量时间序列概率预测的标准方法。尽管具备强大能力，它们在捕捉高维分布和跨特征依赖方面仍存在挑战。近期研究探索了基于扩散或流模型的生成方法，已扩展至时间序列插补和预测任务。然而，可扩展性仍是难题。本文提出一种新方法，结合递归神经网络的高效性与扩散模型的概率建模能力，基于随机插值与带控制特征的条件生成，为该动态领域的发展提供新思路。
原文摘要 · Abstract (English)
Sequential models like recurrent neural networks and transformers have become standard for probabilistic multivariate time series forecasting across various domains. Despite their strengths, they struggle with capturing high-dimensional distributions and cross-feature dependencies. Recent work explores generative approaches using diffusion or flow-based models, extending to time series imputation and forecasting. However, scalability remains a challenge. This work proposes a novel method combining recurrent neural networks' efficiency with diffusion models' probabilistic modeling, based on stochastic interpolants and conditional generation with control features, offering insights for future developments in this dynamic field.
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