Causal relationship analysis of high-dimensional time series based on quantile factor model

Abstract

Finding the causal relationship between variables from observed data is a key issue in many decision analytics researches. Because the traditional Granger causality model is affected by the curse of dimensionality, it is difficult to accurately find causality in high-dimensional time series. To address this problem, we propose a new Granger causality analysis method based on quantile factor model, named QFM-CGC. First, QFM-CGC adopts Akaike information criterion to select models, which avoids setting the lag order relying on human intervention. Then, the quantile factor model is established to reduce the dimensionality of conditional variables in a VAR (Vector Auto-Regressive) model, further reducing the number of coefficients. After that, the reduced-dimensional VAR model is used for a conditional Granger causality analysis. Finally, in order to identify the connectivity structure between the underlying system and observation time series, Monte Carlo simulation is applied to evaluate the performance of different methods. Experiments compared with classical methods on linear simulation systems in different dimensions and two realistic datasets, all demonstrate the effectiveness of QFM-CGC. In simulation experiments, the method proposed in this article has improved the accuracy of identifying causal relationships by an average of 6% and 3.46%. In two real-world data experiments, compared with the optimal comparison methods, QFM-CGC reduced the prediction error of causal relationship sequences.