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

Hui Liu, Liang Huiling, Liu Liwei, Zhao Jia, Ruan Huaijun

Abstract: Highlights•In order to accurately find causal relationships in high-dimensional time series, causal relationship analysis of high-dimensional time series based on quantile factor model is proposed.•Compared with traditional dimension reduction methods, quantile factor model can capture hidden factors, resulting in little difference in time series before and after dimension reduction.•Experiments are conducted on simulated and real-world data, and the results showed that QFM-CGC performed better than the comparative method.

External IDs:dblp:journals/kbs/LiuHLJH24