Mixed autoregressive moving average model for modeling nonlinear time series
非线性时间序列建模的混合自回归滑动平均模型

A mixed autoregressive moving average(MARMA) model is proposed for modeling nonlinear time series.The model consists of K stationary or nonstationary ARMA components.The stationary conditions and autocorrelation function of the MARMA process are investigated.The estimation of parameters is easily performed via expectation maximization(EM) algorithm.The Bayes information criterion(BIC) is used as a tool for the MARMA model selection.The varried feature of conditional distributions of the MARMA model makes it capable of modeling time series with multimodal conditional distributions and with hetero scedasticity.The model is applied to two real data sets and compared with other competing models.The MARMA model appears to capture features of the data better than other competing models do.