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. In this work, we propose an extension of PCMCI+, a state-of-the-art method for causal discovery on regular multivariate time series, to allow for handling irregular time series. Instead of modelling causal relations through fixed-lag dependenci
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- LinkedLinked via arxiv author · 85%Martim Penim →
“Causal Discovery on Irregular Time Series”
- LinkedLinked via arxiv author · 85%Ricardo Ribeiro Pereira →
“Causal Discovery on Irregular Time Series”
- LinkedLinked via arxiv author · 85%Jacopo Bono →
“Causal Discovery on Irregular Time Series”
- LinkedLinked via arxiv author · 85%Hugo Ferreira →
“Causal Discovery on Irregular Time Series”
- LinkedLinked via arxiv author · 85%Mário A. T. Figueiredo →
“Causal Discovery on Irregular Time Series”
- LinkedLinked via arxiv author · 85%Pedro Bizarro →
“Causal Discovery on Irregular Time Series”