(untitled) (Journal of Fiber Bioengineering and Informatics, 2014)
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.