Enterprise AI Analysis
DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data
This paper introduces DCD, a decomposition-based causal discovery framework for multivariate time series. It separates data into trend, seasonal, and residual components, performing component-specific causal analysis. This approach improves accuracy in recovering causal structures under non-stationarity and autocorrelation, outperforming state-of-the-art baselines. DCD offers identifiability guarantees and robust performance even with partial spectral separability violations.
Key Enterprise Metrics
0.79
True Positive Rate
0.38
False Discovery Rate
9.67
SHD (Lower is better)
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DCD Framework Overview
Performance on Synthetic Data
Comparison with Baselines
Arctic Sea Ice Case Study
Enterprise Process Flow
STL Decomposition
→
Trend Analysis (ADF/KPSS)
→
Seasonal Analysis (HSIC)
→
Residual Analysis (Constraint-based CD)
→
Integrate Multi-Scale Causal Graphs
0.79
Mean TPR (True Positive Rate)
| Method | Key Advantages/Limitations |
|---|---|
| DCD (Ours) |
|
| PCMCI+ |
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| DYNOTEARS |
|
Uncovering Climate Dynamics
DCD successfully recovers multi-scale dependencies in Arctic sea ice data, identifying long-term trends, seasonal cycles, and short-term atmospheric/oceanic feedbacks. This provides a physically interpretable causal structure, unlike baselines that produce spurious or overconnected graphs.
Advanced ROI Calculator
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Potential Annual Savings
$0
Hours Reclaimed Annually
0
Your Implementation Roadmap
Phase 1: Data Preprocessing
Clean and format your multivariate time series data for decomposition.
Phase 2: DCD Model Training
Apply DCD framework to decompose series and perform component-specific causal discovery.
Phase 3: Causal Graph Integration
Consolidate component-level graphs into a unified multi-scale causal structure.
Phase 4: Insights & Deployment
Interpret causal graphs for decision-making and deploy DCD for ongoing analysis.
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