A numerical recipe for the computation of stationary stochastic processes’ autocorrelation function

S. Miccichè

Chaos, Solitons & Fractals, 2023, vol. 171, issue C

Abstract: Many natural phenomena exhibit a stochastic nature that one attempts at modelling by using stochastic processes of different types. In this context, often one is interested in investigating the memory properties of the natural phenomenon at hand. This is usually accomplished by computing the autocorrelation function of the numerical series describing the considered phenomenon. Often, especially when considering real world data, the autocorrelation function must be computed starting from a single numerical series: i.e. with a time-average approach.

Keywords: Stochastic processes long-range correlation Langevin equation (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:171:y:2023:i:c:s0960077923003594

DOI: 10.1016/j.chaos.2023.113458

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