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Maximum entropy approach to multivariate time series randomization
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DOI：10.1038/s41598-020-67536-y.png)
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摘要
En 中文
Natural and social multivariate systems are commonly studied through sets of simultaneous and time-spaced measurements of the observables that drive their dynamics, i.e., through sets of time series. Typically, this is done via hypothesis testing: the statistical properties of the empirical time series are tested against those expected under a suitable null hypothesis. This is a very challenging task in complex interacting systems, where statistical stability is often poor due to lack of stationarity and ergodicity. Here, we describe an unsupervised, data-driven framework to perform hypothesis testing in such situations. This consists of a statistical mechanical approach-analogous to the configuration model for networked systems-for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series. We showcase its possible applications with a case study on financial portfolio selection.
Keyword:
CONFIDENCE-INTERVALS
INFORMATION-THEORY
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IF:
3.9
论文数:
28.2W
被引数:
83.5W
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