Seasonalities and cycles in time series: A fresh look with computer experiments

Michel Fliess and Cédric Join ()
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Michel Fliess: LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau] - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique, ALIEN
Cédric Join: Centre Inria de l'Université de Lille - Inria - Institut National de Recherche en Informatique et en Automatique, CRAN - Centre de Recherche en Automatique de Nancy - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique, ALIEN

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Abstract: Recent advances in the understanding of time series permit to clarify seasonalities and cycles, which might be rather obscure in today's literature. A theorem due to P. Cartier and Y. Perrin, which was published only recently, in 1995, and several time scales yield, perhaps for the first time, a clear-cut definition of seasonalities and cycles. Their detection and their extraction, moreover, become easy to implement. Several computer experiments with concrete data from various fields are presented and discussed. The conclusion suggests the application of this approach to the debatable Kondriatev waves.

Keywords: detection; decomposition; cycles; seasonalities; time series; time scales; nonstandard analysis; deseasonalization; Kondriatev waves; extraction; trend (search for similar items in EconPapers)
Date: 2015-12-14
New Economics Papers: this item is included in nep-ets and nep-mac
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Published in Paris Financial Management Conference, PFMC 2015, Dec 2015, Paris, France

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