Bayesian model selection for complex dynamic systems.

ABSTRACT: Time series generated by complex systems like financial markets and the earth's atmosphere often represent superstatistical random walks: on short time scales, the data follow a simple low-level model, but the model parameters are not constant and can fluctuate on longer time scales according to a high-level model. While the low-level model is often dictated by the type of the data, the high-level model, which describes how the parameters change, is unknown in most cases. Here we present a computationally efficient method to infer the time course of the parameter variations from time-series with short-range correlations. Importantly, this method evaluates the model evidence to objectively select between competing high-level models. We apply this method to detect anomalous price movements i

SUBMITTER: Mark C 

PROVIDER: S-EPMC5935699 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature