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• W3100790619 abstract "We present a new method for detecting superdiffusive behaviour and for determining rates of superdiffusion in time series data. Our method applies equally to stochastic and deterministic time series data (with no prior knowledge required of the nature of the data) and relies on one realisation (ie one sample path) of the process. Linear drift effects are automatically removed without any preprocessing. We show numerical results for time series constructed from i.i.d. $alpha$-stable random variables and from deterministic weakly chaotic maps. We compare our method with the standard method of estimating the growth rate of the mean-square displacement as well as the $p$-variation method, maximum likelihood, quantile matching and linear regression of the empirical characteristic function." @default.
• W3100790619 created "2020-11-23" @default.
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• W3100790619 date "2016-12-19" @default.
• W3100790619 modified "2023-10-17" @default.
• W3100790619 title "On the detection of superdiffusive behaviour in time series" @default.
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• W3100790619 doi "https://doi.org/10.1088/1742-5468/aa4f0f" @default.
• W3100790619 hasPublicationYear "2016" @default.
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