DSPR：双流物理残差网络用于可信的工业时间序列预测
DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecasting
AI总结：
本文提出DSPR框架，通过分离稳定的时序模式与受制度影响的残差动态，提升工业时间序列预测的准确性与物理合理性，实验表明其在不同制度下均能保持高预测精度和鲁棒性。
摘要：
准确预测工业时间序列需要在非平稳运行条件下平衡预测精度与物理合理性。现有数据驱动模型在统计性能上表现优异，但难以尊重受制度影响的交互结构和传输延迟等现实系统特性。为解决这一挑战，我们提出了DSPR（双流物理残差网络）预测框架，该框架明确分离稳定的时间模式与受制度影响的残差动态。第一流建模单个变量的统计时间演化。第二流通过两个关键机制关注残差动态：自适应窗口模块估计流依赖的传输延迟，以及物理引导的动态图整合物理先验，学习时间变化的交互结构并抑制虚假相关性。在四个工业基准上实验表明，DSPR在制度转换下持续提升预测精度和鲁棒性，同时保持强物理合理性。它实现了最先进的预测性能，平均守恒精度超过99%，总变化率达到97.2%。除了预测外，学习的交互结构和自适应滞后提供了与已知领域机制一致的可解释见解，如流依赖的传输延迟和风到功率的缩放行为。这些结果表明，通过物理一致的归纳偏差的架构解耦，为可信的工业时间序列预测提供了一条有效路径。此外，DSPR在长期工业部署中展示出的鲁棒性能弥合了先进预测模型与可信自主控制系统之间的差距。
英文摘要：
Accurate forecasting of industrial time series requires balancing predictive accuracy with physical plausibility under non-stationary operating conditions. Existing data-driven models often achieve strong statistical performance but struggle to respect regime-dependent interaction structures and transport delays inherent in real-world systems. To address this challenge, we propose DSPR (Dual-Stream Physics-Residual Networks), a forecasting framework that explicitly decouples stable temporal patterns from regime-dependent residual dynamics. The first stream models the statistical temporal evolution of individual variables. The second stream focuses on residual dynamics through two key mechanisms: an Adaptive Window module that estimates flow-dependent transport delays, and a Physics-Guided Dynamic Graph that incorporates physical priors to learn time-varying interaction structures while suppressing spurious correlations. Experiments on four industrial benchmarks spanning heterogeneous regimes demonstrate that DSPR consistently improves forecasting accuracy and robustness under regime shifts while maintaining strong physical plausibility. It achieves state-of-the-art predictive performance, with Mean Conservation Accuracy exceeding 99% and Total Variation Ratio reaching up to 97.2%. Beyond forecasting, the learned interaction structures and adaptive lags provide interpretable insights that are consistent with known domain mechanisms, such as flow-dependent transport delays and wind-to-power scaling behaviors. These results suggest that architectural decoupling with physics-consistent inductive biases offers an effective path toward trustworthy industrial time-series forecasting. Furthermore, DSPR's demonstrated robust performance in long-term industrial deployment bridges the gap between advanced forecasting models and trustworthy autonomous control systems.
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