Hassani, Hossein (2007): Singular Spectrum Analysis: Methodology and Comparison. Published in: Journal of Data Science , Vol. 5, No. 2 (1. April 2007): pp. 239-257.
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In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. In this paper, the performance of the SSA technique has been considered by applying it to a well-known time series data set, namely, monthly accidental deaths in the USA. The results are compared with those obtained using Box-Jenkins SARIMA models, the ARAR algorithm and the Holt-Winter algorithm (as described in Brockwell and Davis (2002)). The results show that the SSA technique gives a much more accurate forecast than the other methods indicated above.
|Item Type:||MPRA Paper|
|Institution:||Cardiff University and Central Bank of the Islamic Republic of Iran|
|Original Title:||Singular Spectrum Analysis: Methodology and Comparison|
|Keywords:||ARAR algorithm; Box-Jenkins SARIMA models; Holt-Winter algorithm; singular spectrum analysis (SSA); USA monthly accidental deaths series|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General
C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods
|Depositing User:||Hossein Hassani|
|Date Deposited:||22. Sep 2007|
|Last Modified:||01. Jul 2015 14:27|
Alexandrov, Th. and Golyandina, N. (2004). The automatic extraction of time series trend and periodical components with the help of the Caterpillar SSA approach. Exponenta Pro 3-4, 54-61.(In Russian.) Alexandrov, Th. and Golyandina, N. (2004). Thresholds setting for automatic extraction of time series trend and periodical components with the help of the Caterpillar SSA approach. Proc. IV International Conference SICPRO’05, 25-28. Allen, M. R. and Smith, L. A. (1996). Monte Carlo SSA: Detecting irregular oscillations in the presence of coloured noise. Journal of Climate 9, 3373-3404. Box, G. E .P. and Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control, Holden-Day. Brockwell, P. J. and Davis, R. A. (2002). Introduction to Time Series and Forecasting, 2nd edition. Springer. Broomhead, D. S. and King, G. P. (1986). Extracting qualitative dynamics from experimental data. Physica D 20, 217-236. Danilov, D. (1997). Principal components in time series forecast. Journal of Computational and Graphical Statistics 6, 112-121. Danilov, D. and Zhigljavsky, A. (Eds.). (1997). Principal Components of Time Series: the ‘Caterpillar’ method, University of St. Petersburg Press. (In Russian.) Elsner, J. B. and Tsonis, A. A. (1996). Singular Spectral Analysis. A New Tool in Time Series Analysis. Plenum Press. Ghil, M. and Taricco, C. (1997). Advanced spectral analysis methods. In Past and present Variability of the Solar-Terrestrial system: Measurement, Data Analysis and Theoretical Model (Edited by G. C. Castagnoli and A. Provenzale), 137-159. IOS Press. Golyandina, N., Nekrutkin, V. and Zhigljavsky, A. (2001). Analysis of Time Series Structure: SSA and related techniques. Chapman & Hall/CRC. Golyandina, N. and Osipov, E. (2006). Caterpillar SSA method for analysis of time series with missing values. Submitted. Kondrashov, D. and Ghil1, M. (2006). Spatio-temporal filling of missing points in geophysical data sets. Nonlin. Processes Geophys 13, 151-159. Kondrashov, D., Feliks, Y. and Ghil, M. (2005). Oscillatory modes of extended Nile River records (A. D. 622–922), Geophys. Res. Lett 32, L10702, doi:10.1029/2004GL022156. Moskvina, V. G. and Zhigljavsky, A. (2003). An algorithm based on singular spectrum analysis for change-point detection. Communication in Statistics - Simulation and Computation 32, 319-352. Newton, H. J. and Parzen, E. (1984). Forecasting and time series model types of economic time series. In Major Time Series Methods and Their Relative Accuracy (Edited by S. Makridakis, et al.), 267-287, Wiley. Schoellhamer, D. H. (2001). Singular spectrum analysis for time series with missing data. Geophys. Res. Lett 28, 3187-3190. Vautard, R., Yiou, P. and Ghil, M. (1992). Singular-spectrum analysis: A toolkit for short, noisy chaotic signal. Physica D 58, 95-126. Yiou, P., Sornette, D. and Gill, M. (2000). Data-adaptive wavelets and multi-scale singular spectrum analysis. Physica D 142, 254-290.