Abstract
We propose a procedure for testing stationarity of time series by combining a test for time independence of the probability density with one of the spectral density. The potentials of this test procedure are demonstrated by its application to various types of numerically simulated time series ranging from simple linear stochastic processes to high-dimensional transient chaos. Problems of practical implementation are discussed, in particular the relation between the lengths of the time series and its maximal relevant time scales. Stationarity is then tested for experimental data from geophysics and physiology. Exchange rates are found to be stationary on time scales of decades in the sense that their spectral densities do not significantly change.
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Witt, A., Kurths, J. (2002). Testing Stationarity in Time Series. In: Soofi, A.S., Cao, L. (eds) Modelling and Forecasting Financial Data. Studies in Computational Finance, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0931-8_15
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DOI: https://doi.org/10.1007/978-1-4615-0931-8_15
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