Volume 7, Issue 1
Time-Varying Moving Average Model for Autocovariance Nonstationary Time Series

Wanchun Fei & Lun Bai

Journal of Fiber Bioengineering & Informatics, 7 (2014), pp. 53-65.

Published online: 2014-07

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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 efficiency. 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.

  • Keywords

MA Model Autocovariance Parameter Estimation Simulation Model Selection

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@Article{JFBI-7-53, author = {}, title = {Time-Varying Moving Average Model for Autocovariance Nonstationary Time Series}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2014}, volume = {7}, number = {1}, pages = {53--65}, 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 efficiency. 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.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbi03201405}, url = {http://global-sci.org/intro/article_detail/jfbi/4766.html} }
TY - JOUR T1 - Time-Varying Moving Average Model for Autocovariance Nonstationary Time Series JO - Journal of Fiber Bioengineering and Informatics VL - 1 SP - 53 EP - 65 PY - 2014 DA - 2014/07 SN - 7 DO - http://doi.org/10.3993/jfbi03201405 UR - https://global-sci.org/intro/article_detail/jfbi/4766.html KW - MA Model KW - Autocovariance KW - Parameter Estimation KW - Simulation KW - Model Selection AB - 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 efficiency. 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.
Wanchun Fei & Lun Bai. (2019). Time-Varying Moving Average Model for Autocovariance Nonstationary Time Series. Journal of Fiber Bioengineering and Informatics. 7 (1). 53-65. doi:10.3993/jfbi03201405
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