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Learning the Structure of a Nonstationary Vector Autoregression
We adapt graphical causal structure learning methods to apply to nonstationary time series data, specifically to processes that exhibit stochastic trends. We modify the likelihood component of the BIC score used by score-based search algorithms, such that it remains a consistent selection criterion...
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| Published in: | Proc Mach Learn Res |
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| Main Authors: | , |
| Format: | Artigo |
| Language: | Inglês |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6890532/ https://ncbi.nlm.nih.gov/pubmed/31803862 |
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