Xian Qiqi, Chen Zhe Sage
ArXiv. 2024 Nov 13:arXiv:2408.06109v3.
Identifying directed spectral information flow between multivariate time series is important for many applications in finance, climate, geophysics and neuroscience. Spectral Granger causality (SGC) is a prediction-based measure characterizing directed information flow at specific oscillatory frequencies. However, traditional vector autoregressive (VAR) approaches are insufficient to assess SGC when time series have mixed frequencies (MF) or are coupled by nonlinearity. Here we propose a time-frequency canonical correlation analysis approach ("MF-TFCCA") to assess the strength and driving frequency of spectral information flow. We validate the approach with extensive computer simulations on MF time series under various interaction conditions and further assess statistical significance of the estimate with surrogate data. In various benchmark comparisons, MF-TFCCA consistently outperforms the traditional parametric MF-VAR model in both computational efficiency and detection accuracy, and recovers the dominant driving frequencies. We further apply MF-TFCCA to real-life finance, climate and neuroscience data. Our analysis framework provides an exploratory and computationally efficient nonparametric approach to quantify directed information flow between MF time series in the presence of complex and nonlinear interactions.
识别多元时间序列之间的定向频谱信息流对于金融、气候、地球物理学和神经科学等众多应用而言至关重要。频谱格兰杰因果关系（SGC）是一种基于预测的度量，用于表征特定振荡频率下的定向信息流。然而，当时间序列具有混合频率（MF）或由非线性耦合时，传统的向量自回归（VAR）方法不足以评估SGC。在此，我们提出一种时频典型相关分析方法（“MF-TFCCA”）来评估频谱信息流的强度和驱动频率。我们通过在各种相互作用条件下对MF时间序列进行广泛的计算机模拟来验证该方法，并使用替代数据进一步评估估计的统计显著性。在各种基准比较中，MF-TFCCA在计算效率和检测准确性方面均始终优于传统的参数化MF-VAR模型，并能恢复主导驱动频率。我们进一步将MF-TFCCA应用于实际的金融、气候和神经科学数据。我们的分析框架提供了一种探索性且计算高效的非参数方法，用于在存在复杂和非线性相互作用的情况下量化MF时间序列之间的定向信息流。
