arXiv:2507. 11178v3 Announce Type: replace-cross Abstract: With the advancement of deep learning technologies, various neural network-based Granger causality models have been proposed.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2603. 20980v3 Announce Type: replace Abstract: Time-varying causal models provide a powerful framework for studying dynamic scientific systems, yet most existing approaches assume that the underlying causal network is known a priori - an assumption rarely satisfied in real-world domains where causal structure is uncertain, evolving, or only indirectly observable.
arXiv:2602. 18662v2 Announce Type: replace Abstract: Causal discovery for both cross-sectional and temporal data has traditionally followed a dataset-specific paradigm, where a new model is fitted for each individual dataset.
arXiv:2606. 13024v1 Announce Type: cross Abstract: Granger Causal Discovery (GCD) is fundamental for analyzing temporal dependencies in complex systems.
arXiv:2501. 02672v4 Announce Type: replace-cross Abstract: Granger causality (GC) is widely used to infer directed relationships in time-series data.
The paper introduces Causal Local States (CLS), a framework that simultaneously infers an approximate Granger‑causal interaction network and forecasts the dynamics of a system. CLS selects, for each node, the smallest set of neighbors that enables near‑optimal prediction, and then combines these local neighborhoods to forecast the entire system. Experiments on three increasingly difficult benchmarks show that CLS reconstructs the underlying networks with high fidelity and achieves forecast accuracy comparable to a model that uses the true network.
arXiv:2512.07624v2 Announce Type: replace Abstract: Process Model Forecasting (PMF) aims to predict how the control-flow structure of a process evolves over time by modeling the temporal dynamics of...