用五个通用概念和物理规律约束，实现可解释的时间序列预测。
Signals, Concepts, and Laws: Toward Universal, Explainable Time-Series Forecasting
- 基于五个自监督通用概念建模时间序列
- 引入微分方程残差项确保物理合理性
- 适合需要可解释性的工业与科学场景
多变量时间序列的准确、可解释且符合物理规律的预测仍是长期挑战，尤其当其统计特性在不同领域间差异显著时。本文提出DORIC——一种领域通用、基于微分方程正则化、可解释的概念变换器模型，通过五个自监督的、领域无关的概念生成预测，并在训练中引入基于第一性原理的可微残差项，以保证模型输出符合基本物理规律。
原文摘要 · Abstract (English)
Accurate, explainable and physically credible forecasting remains a persistent challenge for multivariate time-series whose statistical properties vary across domains. We propose DORIC, a Domain-Universal, ODE-Regularized, Interpretable-Concept Transformer for Time-Series Forecasting that generates predictions through five self-supervised, domain-agnostic concepts while enforcing differentiable residuals grounded in first-principles constraints.
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