论文标题
多渠道时间序列的动态窗口级别的Granger因果关系
Dynamic Window-level Granger Causality of Multi-channel Time Series
论文作者
论文摘要
Granger因果关系方法分析了时间序列因果关系，而无需构建复杂的因果关系图。但是，传统的Granger因果关系假设因果关系位于时间序列通道之间并保持恒定，这无法在时间序列通道沿着动态因果关系建模现实世界中的时间序列数据。在本文中，我们介绍了多通道时间序列数据的动态窗口级Granger因果关系（DWGC）。我们通过在滑动窗口上使用预测错误进行f检验，在窗口级上构建因果关系模型。我们在DWGC方法中提出了因果关系索引技巧，以重新重量原始时间序列数据。从本质上讲，因果关系指数是减少自动相关并增加互相关的因果效应，从而改善了DWGC方法。对两个合成和一个现实世界数据集的理论分析和实验结果表明，改进的DWGC方法和因果关系索引更好地检测了窗口级的因果关系。
Granger causality method analyzes the time series causalities without building a complex causality graph. However, the traditional Granger causality method assumes that the causalities lie between time series channels and remain constant, which cannot model the real-world time series data with dynamic causalities along the time series channels. In this paper, we present the dynamic window-level Granger causality method (DWGC) for multi-channel time series data. We build the causality model on the window-level by doing the F-test with the forecasting errors on the sliding windows. We propose the causality indexing trick in our DWGC method to reweight the original time series data. Essentially, the causality indexing is to decrease the auto-correlation and increase the cross-correlation causal effects, which improves the DWGC method. Theoretical analysis and experimental results on two synthetic and one real-world datasets show that the improved DWGC method with causality indexing better detects the window-level causalities.
