# Inferring directed spectral information flow between mixed-frequency time series

## TL;DR

This paper introduces a new method to analyze how information flows between time series with mixed frequencies, outperforming traditional approaches in accuracy and efficiency.

## Contribution

The paper proposes a nonparametric method (MF-TFCCA) for assessing directed spectral information flow in mixed-frequency time series with nonlinear interactions.

## Key findings

- MF-TFCCA outperforms traditional parametric models in computational efficiency and detection accuracy.
- The method successfully recovers dominant driving frequencies in mixed-frequency time series.
- MF-TFCCA is validated on real-world data from finance, climate, and neuroscience.

## Abstract

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.