AI RESEARCH
DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data
arXiv CS.LG
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ArXi:2602.01433v2 Announce Type: replace Multivariate time series in domains such as finance, climate science, and healthcare often exhibit long-term trends, seasonal patterns, and short-term fluctuations, complicating causal inference under non-stationarity and autocorrelation. Existing causal discovery methods typically operate on raw observations, making them vulnerable to spurious edges and misattributed temporal dependencies. We