Abstract
We present a method for the detection of periodic motions in long-time series of data. In contrast to other commonly used approaches, e.g., empirical orthogonal functions, principal oscillation patterns, singular spectrum analyses, this method is oriented towards scalar-quantities, therefore, it does not require the introduction of an arbitrary metric in the space of the dynamical variables. Nonlinear effects are included; needless to say, the higher the non-linearities included the longer and more complicated the actual calculations become. We are trying the method on a Lorenz model and on a simple model of the dynamics of the atmosphere.
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© 1994 Springer Science+Business Media Dordrecht
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Pasmanter, R.A. (1994). Searching for Periodic Motions in Long-Time Series. In: Grasman, J., van Straten, G. (eds) Predictability and Nonlinear Modelling in Natural Sciences and Economics. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-0962-8_6
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DOI: https://doi.org/10.1007/978-94-011-0962-8_6
Publisher Name: Springer, Dordrecht
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