The analysis of non-Gaussian time series has been studied extensively and has many applications. Many successful models can be viewed as special cases or variations of the generalized autoregressive moving average (GARMA) models of Benjamin et al. (2003), where a link function similar to that used in generalized linear models is introduced and the conditional mean, under the link function, assu...

A time-series autoregressive moving average (ARMA) approach was used to develop stochastic models of tree crown profiles for five conifer species of the Sierran mixed conifer habitat type. Models consisted of three components: (1) a polynomial trend; (2) an ARMA model; and (3) random error. A Bayesian information criterion was used to evaluate alternative models. It was found that 70% of the cr...

The classiication of High Range Resolution (HRR) radar signatures using multi-scale features is considered. We present a hierarchical autoregressive moving average (ARMA) model for modeling HRR radar signals at multiple scales, and use spectral features extracted from the model for classifying radar signatures. First, we show that the radar signal at a diierent scale follows an ARMA process if ...

A new method is introduced,for the identification of Wiener model. The Wiener model consists of a linear,dynamic! block followed by a static nonlinearity. The nonlinearity and the linear dynamic part in the model are identified by using radial basis functions neural network (RBFNN) and autoregressive moving average (ARMA) model, respectively. The new algorithm makes use of the well known mappin...

Coventry University, Coventry, CV1 5FB, U.K In this article, we build Box-Jenkins ARMA model and ARMA-GARCH model to forecast the returns of shanghai stock exchange composite index in financial engineering. Out-of-sample forecasting performances are evaluated to compare the forecastability of the two models. Traditional engineering type of models aim to minimize statistical errors, however, the...

This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition MEMD is employed here for multiband representation of multichannel financial time series together. Autoregressivemoving average ARMA model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the over...

In this paper, we purpose a theoretical development of a metric for speech classification based on cepstral features obtained from ARMA models. Thus working with an ARMA model as a complex rational function, is possible to define a metric d(M,M´) between two stable ARMA models M, M´by means of the cepstrum coefficients of the models. This metric may be calculated algorithmically as a finite sum...

This paper discusses the stochastic process structure of certain differential transformations (OTis) associated with perfectly observed ARMA processes and uses DT's to obtain the asymptotic information matrix for possibly non-Gaussian situations. The DT's can also be applied to implement approximate M-estimate algorithms for the ARMA model parameters. M-estimates yield asymptotic efficieQcy rob...

The presence of outliers in time series can seriously affect the model specification and parameter estimation. To avoid these adverse effects, it is essential to detect these outliers and remove them from time series. By the Bayesian statistical theory, this article proposes a method for simultaneously detecting the additive outlier (AO) and innovative outlier (IO) in an autoregressive moving-a...