Persistence of Non-Markovian Gaussian Stationary Processes in Discrete Time

Abstract

The persistence of a stochastic variable is the probability that it does not cross a given level during a fixed time interval. Although persistence is a simple concept to understand, it is in general hard to calculate. Here we consider zero mean Gaussian stationary processes in discrete time $n$. Few results are known for the persistence $P_0(n)$ in discrete time, except the large time behavior which is characterized by the nontrivial constant $\theta$ through $P_0(n)\sim {\theta }^n$. Using a modified version of the Independent Interval Approximation (IIA) that we developed before, we are able to calculate $P_0(n)$ analytically in $z$-transform space in terms of the autocorrelation function $A(n)$. If $A(n)\to 0$ as $n\to \infty$, we extract $\theta$ numerically, while if $A(n)=0$, for finite $n>N$, we find $\theta$ exactly (within the IIA). We apply our results to three special cases: the nearest neighbor-correlated "first order moving average process" where $A(n)=0$ for $n>1$, the double exponential-correlated "second order autoregressive process" where $A(n)=c_1{\lambda }_1^n+c_2{\lambda }_2^n$, and power law-correlated variables where $A(n)\sim n^{-\mu }$. Apart from the power-law case when $\mu <5$, we find excellent agreement with simulations.

arXiv comment7 pages, 4 figures

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