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We report a general technique to study a given experimental time series withsuperstatistics. Crucial for the applicability of the superstatistics conceptis the existence of a parameter $\beta$ that fluctuates on a large time scaleas compared to the other time scales of the complex system under consideration.The proposed method extracts the main superstatistical parameters out of agiven data set and examines the validity of the superstatistical modelassumptions. We test the method thoroughly with surrogate data sets. Then theapplicability of the superstatistical approach is illustrated using realexperimental data. We study two examples, velocity time series measured inturbulent Taylor-Couette flows and time series of log returns of the closingprices of some stock market indices.

Superstatistical fluctuations in time series: Applications to share-price dynamics and turbulence