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Average Monthly Temperature Forecast In Romania By Using Singular Spectrum Analysis

Author

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  • MARINOIU CRISTIAN

    (PETROLEUM-GAS UNIVERSITY OF PLOIESTI)

Abstract

Singular spectrum analysis (SSA) is one of the relatively recent time series analysis method which does not require a-priori assumption of a particular model. The method is based on the classical results in mathematics and has the advantage that it relies on the estimation of only two parameters. This paper briefly describes the main steps of the method and its use for forecast the time series of average monthly temperature in Romania. At the same time predictions with the same time series are made by using two other known forecast methods. By comparing methods in terms of prediction error we may find that using SSA leads to the best results.

Suggested Citation

  • Marinoiu Cristian, 2018. "Average Monthly Temperature Forecast In Romania By Using Singular Spectrum Analysis," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 3, pages 48-57, June.
    • Handle: RePEc:cbu:jrnlec:y:2018:v:3:p:48-57
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    References listed on IDEAS

    as
    1. Christina Beneki & Bruno Eeckels & Costas Leon, 2012. "Signal Extraction and Forecasting of the UK Tourism Income Time Series: A Singular Spectrum Analysis Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(5), pages 391-400, August.
    2. Hassani, Hossein, 2007. "Singular Spectrum Analysis: Methodology and Comparison," MPRA Paper 4991, University Library of Munich, Germany.
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