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cs.LG2025
InvarGC: Invariant Granger Causality for Heterogeneous Interventional Time Series under Latent Confounding
Ziyi Zhang, Shaogang Ren, Xiaoning Qian +1
Granger causality is widely used for causal structure discovery in complex systems from multivariate time series data. Traditional Granger causality tests based on linear models of…
cs.LG2024
Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data
Ziyi Zhang, Shaogang Ren, Xiaoning Qian +1
Granger causality, commonly used for inferring causal structures from time series data, has been adopted in widespread applications across various fields due to its intuitive expla…
cs.LG2024
Towards Invariant Time Series Forecasting in Smart Cities
Ziyi Zhang, Shaogang Ren, Xiaoning Qian +1
In the transformative landscape of smart cities, the integration of the cutting-edge web technologies into time series forecasting presents a pivotal opportunity to enhance urban p…