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| Time-Varying Moving Average Model for Autocovariance Nonstationary Time Series |
| Wanchun Fei, Lun Bai |
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Abstract In time series analysis, fitting the Moving Average (MA) model is more complicated than Autoregressive (AR) models because the error terms are not observable. This means that iterative nonlinear fitting procedures need to be used in place of linear least squares. In this paper, Time-Varying Moving Average (TVMA) models are proposed for an autocovariance nonstationary time series. Through statistical analysis, the parameter estimates of the MA models demonstrate high statistical effciency. The Akaike Information Criterion (AIC) analyses and the simulations by the TVMA models were carried out. The suggestion about the TVMA model selection is given at the end. This research is useful for analyzing an autocovariance nonstationary time series in theoretical and practical fields.
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| Cite this article: |
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Wanchun Fei,Lun Bai. Time-Varying Moving Average Model for Autocovariance Nonstationary Time Series[J]. Journal of Fiber Bioengineering and Informatics, 2014, 7(1): 53-65.
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[1] Box GEP, Jenkins GM, Reinsel GC. Time Series Analysis, forecasting and control. Post and Telecom-munication Press, Beijing: 2005. p. 46-130.
[2] Priestley MB. Spectral analysis and time series, Volume 2 Multivariate series, prediction and control. Academic Press, London. 1981. p. 816-890.
[3] Priestley MB. Non-linear and non-stationary time series analysis. Academic Press, London: 1988. p. 140-183.
[4] Fan J, Yao Q. Nonlinear time series: nonparametric and parametric methods. Springer, New York: 2005. p. 89-124.
[5] Dahlhaus R. Fitting time series models to nonstationary processes. Ann Statist: 1997: 1: 1-37.
[6] Dahlhaus R, Giraitis L. On the optimal segment length for parameter estimates for locally sta-tionary time series. J Time Series Anal, 1998: 6: 629-655.
[7] Guo W, Dai M. Multivariate time-dependent spectral analysis using cholesky decomposition. Sta-tistica Sinica: 2006: 825-845.
[8] Davis RA, Lee TCM, Rodriguez-Yam Gabriel A. Structural break estimation for nonstationary time series models. J Am Stat Assoc: 2006: 223-239.
[9] Fei W, Bai L. Statistical characteristics of nonstationary time series. Int J Nonlin Sci Num: Sup-plement. 2010: 11: 295-299.
[10] Fei W, Bai L. Auto-regressive models of non-stationary time series with finite length. Tsinghua Sci Technol: 2005: 2: 162-168.
[11] Fei W, Bai L, Morikawa H, Miura M. Simulation of size series of cocoon filament with time varying parameter autoregressive model. J Silk Sci Tech Jpn: 2005: 87-91.
[12] Fei W, Bai L. Pattern recognition of non-stationary time series with finite length. Tsinghua Sci Technol: 2006: 5: 611-616.
[13] Grenier Y. Time-dependent ARMA modeling of nonstationary signals. IEEE Transactions on Acoustics, Speech, and Signal Processing: ASSP31(4) 1983: 899-911.
[14] Fei W, Bai L. Time-varying parameter auto-regressive models for autocovariance nonstationary time series. Sci China Ser A: 2009: 3: 577-584.
[15] Akaike H. Information theory and an extention of the maximum likelihood principle. Petrov B. N. and Csaki F. (eds.), 2nd Inter. Symp. on Information Theory, Akademiai Kiado, Budapest: 1973. p. 267-281.
[16] Akaike H. A new look at the statistical model identification. IEEE Trans. Autom. Contr: 1974: AC-19: 716-723. |
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2014, Vol. 7 
