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Complex Exponential Smoothing

Author

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  • Svetunkov, Ivan
  • Kourentzes, Nikolaos

Abstract

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.

Suggested Citation

  • Svetunkov, Ivan & Kourentzes, Nikolaos, 2015. "Complex Exponential Smoothing," MPRA Paper 69394, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:69394
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    File URL: https://mpra.ub.uni-muenchen.de/69394/1/MPRA_paper_69394.pdf
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    References listed on IDEAS

    as
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    Cited by:

    1. Sergey G. Svetunkov, 2021. "Short-Term Economic Forecasting by Complex-Valued Autoregressions," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 4.

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    More about this item

    Keywords

    Forecasting; exponential smoothing; ETS; model selection; information potential; complex variables;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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