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Friday, July 6, 2012

Scaling properties of correlated random walks

Many stochastic time series can be modelled by discrete random walks in which a step of random sign but constant length \delta x is performed after each time interval \delta t. In correlated discrete time random walks (CDTRWs), the probability q for two successive steps having the same sign is unequal 1/2. The resulting probability distribution P(\Delta x,\Delta t) that a displacement \Delta x is observed after a lagtime \Delta t is known analytically for arbitrary persistence parameters q. In this short note we show how a CDTRW with parameters \left[  \delta t, \delta x, q \right] can be mapped onto another CDTRW with rescaled parameters \left[  \delta t/s, \delta x\cdot g(q,s), q^{\prime}(q,s) \right], for arbitrary scaling parameters s, so that both walks have the same displacement distributions P(\Delta x,\Delta t) on long time scales. The nonlinear scaling functions g(q,s) and q^{\prime}(q,s) and derived explicitely. This scaling method can be used to model time series measured at discrete sample intervals \delta t but actually corresponding to continuum processes with variations occuring on a much shorter time scale \delta t/s.

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