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Complex Exponential Smoothing

Listed author(s):
  • Svetunkov, Ivan
  • Kourentzes, Nikolaos
Registered author(s):

    Exponential smoothing has been one of the most popular forecasting methods for business and industry. Its simplicity and transparency have made it very attractive. Nonetheless, modelling and identifying trends has been met with mixed success, resulting in the development of various modifications of trend models. We present a new approach to time series modelling, using the notion of ``information potential" and the theory of functions of complex variables. A new exponential smoothing method that uses this approach, ``Complex exponential smoothing" (CES), is proposed. It has an underlying statistical model described here and has several advantages over the conventional exponential smoothing models: it allows modelling and forecasting both trended and level time series, effectively sidestepping the model selection problem. CES is evaluated on real data demonstrating better performance than established benchmarks and other exponential smoothing methods.

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    File URL: https://mpra.ub.uni-muenchen.de/69394/1/MPRA_paper_69394.pdf
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    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 69394.

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    Date of creation: 01 May 2015
    Handle: RePEc:pra:mprapa:69394
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