This review addresses a central question in the field of complex systems: given a fluctuating
(in time or space), sequentially measured set of experimental data, how should one analyze …

One of the most important problems in meteorology, physical oceanography, climatology,
and related fields is the understanding and dynamical description of multi-scale interactions …

We apply recent methods in stochastic data analysis for discovering a set of few stochastic
variables that represent the relevant information on a multivariate stochastic system, used as …

To improve the estimation of drift and diffusion coefficients of stochastic processes in case of
a limited amount of usable data due to eg non-stationarity of natural systems we suggest to …

The application of stochastic differential equations (SDEs) to the analysis of temporal data
has attracted increasing attention, due to their ability to describe complex dynamics with …

The complex dynamics of physical systems can often be modeled with stochastic differential
equations. However, computational constraints inhibit the estimation of dynamics from large …

It is a big challenge in the analysis of experimental data to disentangle the unavoidable
measurement noise from the intrinsic dynamical noise. Here we present a general …

An alternative approach for the analysis of Langevin-type stochastic processes in the
presence of strong measurement noise is presented. For the case of Gaussian distributed …

This paper presents a direct method to obtain the deterministic and stochastic contribution of
the sum of two independent stochastic processes, one of which is an Ornstein–Uhlenbeck …

One very important topic in climatology, meteorology, and related fields is the detailed
understanding of extremes in a changing climate. There is broad consensus that the most …