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Forecasting mortality rate by multivariate singular spectrum analysis

Author

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  • Rahim Mahmoudvand
  • Dimitrios Konstantinides
  • Paulo Canas Rodrigues

Abstract

In this paper, we investigate the possibility of using multivariate singular spectrum analysis (SSA), a nonparametric technique in the field of time series analysis, for mortality forecasting. We consider a real data application with 9 European countries: Belgium, Denmark, Finland, France, Italy, Netherlands, Norway, Sweden, and Switzerland, over a period 1900 to 2009, and a simulation study based on the data set. The results show the superiority of multivariate SSA in comparison with the univariate SSA, in terms of forecasting accuracy.

Suggested Citation

  • Rahim Mahmoudvand & Dimitrios Konstantinides & Paulo Canas Rodrigues, 2017. "Forecasting mortality rate by multivariate singular spectrum analysis," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(6), pages 717-732, November.
    • Handle: RePEc:wly:apsmbi:v:33:y:2017:i:6:p:717-732
    DOI: 10.1002/asmb.2274
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    Cited by:

    1. Sulandari, Winita & Subanar, & Lee, Muhammad Hisyam & Rodrigues, Paulo Canas, 2020. "Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks," Energy, Elsevier, vol. 190(C).
    2. Paulo Canas Rodrigues & Olushina Olawale Awe & Jonatha Sousa Pimentel & Rahim Mahmoudvand, 2020. "Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks," Stats, MDPI, vol. 3(2), pages 1-21, June.
    3. Silva, Emmanuel Sirimal & Hassani, Hossein, 2022. "‘Modelling’ UK tourism demand using fashion retail sales," Annals of Tourism Research, Elsevier, vol. 95(C).

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