arXiv Machine Learning
Jul 21

Causal Discovery on Irregular Time Series

arXiv:2607. 18226v1 Announce Type: new Abstract: Causal discovery methods have shown strong performance in temporal systems, but they typically rely on regular and discrete lag structures, limiting their applicability to regularly sampled data.

By Martim Penim, Ricardo Ribeiro Pereira, Jacopo Bono, Hugo Ferreira, M\'ario A. T. Figueiredo, Pedro Bizarro
Hugging Face Trending Papers
Jul 20

Causal Discovery on Irregular Time Series

Causal discovery methods have shown strong performance in temporal systems, but they typically rely on regular and discrete lag structures, limiting their applicability to regularly sampled data. However, many real-world tasks require dealing with irregularly sampled streams of events, such as sensor streams, healthcare data, and financial transactions.

arXiv AI
Sep 7

Beyond Stationarity in Time Series: Discovering Causal Structures and Latent Regimes via Markov Blankets

The paper presents RCBNB-MB, a causal discovery algorithm that relaxes the assumption of a single, time‑consistent causal structure in time series. It identifies latent causal regimes—subsets of time points where a stable causal graph holds—and iteratively segments the series to recover both regime transitions and the corresponding causal graphs using Markov blankets. The authors provide theoretical guarantees and demonstrate through simulations and real IT monitoring data that RCBNB-MB outperforms baseline methods in detecting regime changes and their causal structures.

By Lei Zan, Charles K. Assaad, Emilie Devijver, Eric Gaussier
arXiv AI
Sep 25

Learning Causal Structure of Time Series using Best Order Score Search

The paper introduces TS‑BOSS, a time‑series extension of the Best Order Score Search (BOSS) algorithm for causal structure learning. TS‑BOSS conducts a permutation‑based search over dynamic Bayesian network structures, using grow‑shrink trees to cache intermediate score computations, thereby maintaining scalability and strong empirical performance. The authors provide theoretical guarantees of soundness under suitable assumptions and demonstrate that TS‑BOSS achieves higher adjacency recall than standard constraint‑based methods, especially in high auto‑correlation regimes.

By Irene Gema Castillo Mansilla, Urmi Ninad