提出新方法衡量时间序列预测中各滞后项的重要性。
Autorelevance function and other feature relevance measures for univariate time series
- 基于影子变量与谢林值，定义自相关重要性函数
- 在模拟与真实数据中均准确识别滞后结构
- 适合时序模型解释，尤其适用于递归网络
我们提出一种模型无关的方法，用于衡量机器学习时间序列预测模型中的滞后重要性。结合影子变量、谢林值和可加重要性度量框架，引入自相关重要性和部分自相关重要性函数作为滞后重要性的量化指标。此外，提出一种新方法，用同一模型的一步预测替换联盟方法中缺失特征。我们在多种模拟和真实数据场景下评估了这些方法，结果表明该组合框架特别适用于时间序列分析。通过使用季节性ARMA族模型和循环神经网络的多个模型实例验证，计算出的相关性度量几乎在所有情况下均成功展示了预期的滞后结构。
原文摘要 · Abstract (English)
We propose a model agnostic methodology to measure lag relevance in machine learning forecasting models applied to univariate time series. Particularly, we are working in the context of time series using the frameworks of Ghost variables and Shapley values, together with additive importance measures, to introduce the auto-relevance and partial auto-relevance functions as the lag importance values. Additionally, we propose a novel method to replace absent features in coalition based methods with a one step forecast from the same model. We evaluate these proposals under different simulations and real data cases. This combined framework perspective is particularly suitable for time series. In addition, to show our discoveries we use a pull of models from the seasonal ARMA family and recurrent neural networks. We found that the calculated relevance measures successfully demonstrate the expected lag structure in almost all cases.
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