Learning the Structure of a Nonstationary Vector Autoregression.
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ABSTRACT:
SUBMITTER: Malinsky D
PROVIDER: S-EPMC6890532 | biostudies-literature | 2019 Apr
REPOSITORIES: biostudies-literature
Publications
Learning the Structure of a Nonstationary Vector Autoregression.
Malinsky Daniel D Spirtes Peter P
Proceedings of machine learning research 20190401
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 for integrated or cointegrated processes. We use this modified score in conjunction with the SVAR-GFCI algorithm [15], which allows us to recover qualitative structural information about the underlyin ...[more]
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