Stochastic Modelling with few Parameters

Philipp Meyer, UFS Data-centric Sciences, University of Potsdam Campus Golm, Building 9, Room 0.1210:15-11:15

Simple stochastic models can describe the fluctuations of a time series. A good way to get a first impression of the data is to look at the mean squared displacement. It describes how strong the data fluctuates depending on the time scale and thus enables us to find a model that looks like the data. When looking at complex systems, the model can be improved by including the most important features of the data. In this way the model becomes more complex, but also more physical. I will show applications to political polling data and temperature measurements.