Difficulties of identification for multivariable controlled autoregressive moving average (ARMA) systems lie in that there exist unknown noise terms in the information vector, and the iterative identification can be used for the system with unknown terms in the information vector. By means of the hierarchical identification principle, those noise terms in the information vector are replaced wit...

The needs of accuracy of machines are strictly increasing for the manufacturing processes. It is costly to use high precision machine to achieve the goal. Therefore, if the forecasting of errors can be obtained from the gathered past error values, it allows the control system to compensate the errors. In this paper, the simulations of manufacturing processes are using the ARMA and ARMAX models....

Speech synthesizers based on paramedic methods, still have not achieved the expected naturalness. This is due to less consideration on linear time variant nature between the neighbor phonemes. This paper presents a study to model the phoneme transitions between neighbor phonemes with lesser number of parameters using Auto Regressive Moving Average (ARMA) model, where Steiglitz-McBride algorithm...

This paper investigates the impact of dependent but uncorrelated innovations (errors) on the traditional autoregressive moving average model (ARMA) order determination schemes such as autocorrelation function (ACF), partial autocorrelation function (PACF), extended autocorrelation function (EACF) and unit-root test. The ARMA models with iid innovations have been studied extensively and are well...

When it comes to measuring inflation persistence, a common practice in empirical research is to estimate univariate autoregressive moving average (ARMA) time series models and measure persistence as the sum of the estimated AR coefficients. We examine four potential sources of lag dynamics in inflation: the evolution of policymakers‟ willingness to stabilize output, shifts in the mean inflation...

This study present a hybrid method of estimating VAR, combining Neural Network and ARMA. Empirical results demonstrate that the hybrid method obtained superior results to the conventional method in estimating VAR. When applied to the Shanghai stock market both the conventional and hybrid methods performed well in terms of accuracy, with the only poorly performing result being the HS method in S...

Discrete-time ARMA processes can be placed in a one-to-one correspondence with a set of continuous-time processes that are bounded in frequency by the Nyquist value of π radians per sample period. It is well known that, if data are sampled from a continuous process of which the maximum frequency exceeds the Nyquist value, then there will be a problem of aliasing. However, if the sampling is too...

The interaction between ultrasonic waves and biological tissues is the cause of cavitation phenomena. A primary scope of this paper was to set-up a measurement system for the generation of cavitation by means of forcing a low frequency (20 kHz). From the spectra and power density was evident the presence of the random component associated to the noise of cavitation was evident. For the estimate...

We address the problem of estimating changes in fractional integrated ARMA (FARIMA) processes. These changes may be in the Long Range Dependence (LRD) parameter or the ARMA parameters. The signal is divided into “elementary” segments: the objective is then to estimate the segments in which the changes occur. This estimation is achieved by minimizing a penalized least-squares criterion based on ...