用局部代理模型解释时间序列预测修正，揭示误差背后的规律。
Surrogate Modeling for Explainable Predictive Time Series Corrections
- 以基线模型误差为输入，构建可解释的修正代理模型。
- 通过参数差异分析，量化修正对原始模型的影响。
- 适合需要理解预测偏差成因的研究者和工业应用。
我们提出一种局部代理方法，用于可解释的时间序列预测修正。该方法利用一个初始非可解释的预测模型，对经典时间序列‘基线模型’的预测结果进行改进。通过将基线模型重新拟合到已去除误差预测的数据上，得到模型参数的变化，从而实现对修正过程的可解释性。我们提供了示范性案例，展示该方法在发现并解释数据中潜在模式方面的潜力。
原文摘要 · Abstract (English)
We introduce a local surrogate approach for explainable time-series forecasting. An initially non-interpretable predictive model to improve the forecast of a classical time-series 'base model' is used. 'Explainability' of the correction is provided by fitting the base model again to the data from which the error prediction is removed (subtracted), yielding a difference in the model parameters which can be interpreted. We provide illustrative examples to demonstrate the potential of the method to discover and explain underlying patterns in the data.
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