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Bayesian model selection for complex dynamic systems
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DOI：10.1038/s41467-018-04241-5.png)
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Abstract
En 中文
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 in financial markets, characterize cancer cell invasiveness, identify historical policies relevant for working safety in coal mines, and compare different climate change scenarios to forecast global warming.
Keywords:
CHAIN MONTE-CARLO
HIDDEN MARKOV-MODELS
SUPERSTATISTICAL ANALYSIS
STOCHASTIC VOLATILITY
CHANGE-POINT
TIME-SERIES
DISTRIBUTIONS
FLUCTUATIONS
PROJECTIONS
LIKELIHOOD
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