–In the present work two popular methods for modelling a system are compared using modelling of Air Separation Unit (ASU). To Model any MIMO system in general both ARMA and Subspace Identification methods can be used, and the optimum results can be achieved only after thorough investigation in the actual process of the system and the methodology by which the modelling can be done so as to suit ...

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...

A mean square error criterion is used in this paper to provide a systematic approach to approximate a long-memory time series by a short-memory ARMA(1; 1) process. Analytic expressions are derived to assess the accuracy of such an approximation. These results are valid not only for the pure fractional noise case, but also for a general autoregressive fractional moving average long-memory time s...

There are many situations in which indicators of changes or anomalies in communication networks can be helpful, e.g. in the identification of faults. A dynamic communication network is characterised as a series of graphs with vertices representing IP addresses and edges representing information exchange between these entities weighted by packets sent. Ten graph distance metrics are used to crea...

The purpose of this paper is to analyze in bivariate vector autoregression the relationship between feedback in stochastic systems, Granger causality and a measure of dissimilarity between ARMA models. In particular, we consider a bivariate vector autoregressive processes of order p (a bivariate VAR(p) process) and we prove if the distance between the univariate ARMA models implied by the VAR r...

A temperature prediction method of Insulated Gate Bipolar Transistor (IGBT) module based on autoregressive moving average model is proposed. Historical and current temperature datum of IGBT module is indispensable to the ARMA method, temperature time series is obtained by uniform sampling, and autoregressive (AR) model is constructed. Temperature time series prediction of IGBT module is realize...

Recent results have pointed out the importance of inducing cyclostationarity at the transmitter for blind identification of FIR communication channels. The present paper considers the blind identification problem of an ARMA(p; q) channel by exploiting the cyclostationarity induced at the transmitter through periodic encoding of the input. It is shown that causal and stable ARMA(p; q) channels c...

This paper established the ARMA-GJR-AL model of dynamic risk VaR and CVaR measurement. Considering from aspects of the correlation and volatility and residual distribution characteristics, studying the dynamic risk measures of VaR and CVaR based on ARMA-GJR-AL model. Through empirical research, Risk prediction and accuracy of inspection are given of the Shanghai stock market and the New York st...

Autoregressive moving average (ARMA) modeling has been used in many fields. This paper presents an approach to time series analysis of a general ARMA model parameters estimation. The proposed technique is based on the singular value decomposition (SVD) of a covariance matrix of a third order cumulants from only the output sequence. The observed data sequence is corrupted by additive Gaussian no...

In this paper we propose a test for a set of linear restrictions in a Vector Autoregressive Moving Average (VARMA) model. This test is based on the autoregressive metric, a notion of distance between two univariate ARMA models, M0 and M1, introduced by Piccolo in 1990. In particular, we show that this set of linear restrictions is equivalent to a null distance d(M0,M1) between two given ARMA mo...