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Feasibility of Mortality Forecasting with Singular Spectrum Analysis. |
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| | تاریخ ارسال: 1397/10/18 | |
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 Mahmoudvand, R.; Alhosseini, F.; and Zokaei, M. (2013).
Feasibility of Mortality Forecasting with Singular Spectrum Analysis.
Journal of Data Science, Vol. 11, No. 4, pp. 851-866.
Abstract:
The Lee-Carter model and its extensions are the most popular methods in the field of forecasting mortality rate. But, in spite of introducing several different methods in forecasting mortality rate so far, there is no general method applicable to all situations. Singular Spectrum Analysis (SSA) is a relatively new, powerful and nonparametric time series analysis that its capability in forecasting different time series has been proven in the various sciences. In this paper, we investigate the feasibility of using the SSA to construct mortality forecasts. We use the Hyndman-Ullah model, which is a new extension of Lee-Carter model, as a benchmark to evaluate the performance of the SSA for mortality forecasts in France data sets.
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Forecasting Mortality Rate by Singular Spectrum Analysis. |
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| | تاریخ ارسال: 1397/10/18 | |
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Mahmoudvand, R.; Alehosseini, F.; Rodrigues, C. P. (2015).
Forecasting Mortality Rate by Singular Spectrum Analysis.
RevStat-Statistical Journal, Vol. 13, No. 3, pp. 193-206.
Abstract:
Singular spectrum analysis (SSA) is a relatively new and powerful non-parametric time series analysis technique that has demonstrated its capability in forecasting different time series in various disciplines. In this paper, we study the feasibility of using the SSA to perform mortality forecasts. Comparisons are made with the Hyndman–Ullah model, which is a new powerful tool in the field of mortality forecasting, and will be considered as a benchmark to evaluate the performance of the SSA for mortality forecasting. We use both SSA and Hyndman–Ullah models to obtain 10 forecasts for the period 2000–2009 in nine European countries including Belgium, Denmark, Finland, France, Italy, The Netherlands, Norway, Sweden and Switzerland. Computational results show a superior accuracy of the SSA forecasting algorithms, when compared with the Hyndman–Ullah approach.
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Forecasting Mortality Rate by Multivariate Singular Spectrum Analysis. |
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| | تاریخ ارسال: 1397/10/18 | |
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Mahmoudvand, R.; Konstantinides, G. Dimitrios.; Rodrigues, P.C. (2017).
Forecasting Mortality Rate by Multivariate Singular Spectrum Analysis.
Applied Stochastic Models in Business and Industry, Vol. 33, No. 6. pp. 717-732.
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.
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