DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data

• URL: http://arxiv.org/abs/2602.01433v1
• Date: Sun, 01 Feb 2026 20:45:05 GMT
• Title: DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data
• Authors: Muhammad Hasan Ferdous, Md Osman Gani,
• Abstract summary: Time series in domains such as finance, climate science, and healthcare often exhibit long-term trends, seasonal patterns, and short-term fluctuations.<n>Existing causal discovery methods operate on raw observations, making them vulnerable to spurious edges and misattributed temporal dependencies.<n>We introduce a decomposition-based causal discovery framework that separates each time series into trend, seasonal, and residual components.
• Score: 2.681371732194511
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 introduce a decomposition-based causal discovery framework that separates each time series into trend, seasonal, and residual components and performs component-specific causal analysis. Trend components are assessed using stationarity tests, seasonal components using kernel-based dependence measures, and residual components using constraint-based causal discovery. The resulting component-level graphs are integrated into a unified multi-scale causal structure. This approach isolates long- and short-range causal effects, reduces spurious associations, and improves interpretability. Across extensive synthetic benchmarks and real-world climate data, our framework more accurately recovers ground-truth causal structure than state-of-the-art baselines, particularly under strong non-stationarity and temporal autocorrelation.

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