Model identification is an important and complicated step within the autoregressive integrated moving average (ARIMA) methodology framework. This step is especially difficult for integrated series. In this article first investigate Box-Jenkins methodology and its faults in detecting model, and hence have discussed the problem of outliers in time series. By using this optimization method, we wil...

Most of time series that appear in many economical geophysical and other phenomena are driven by nonGaussian white noise ( ), in this paper investigate some probabilistic properties of Gaussian and nonGaussian mixed with identification methods of ARMA model. We have theoretically derived the characteristic function the first of (four moments) of skeweness and kurtosis coefficients for white noi...

Conventionally, the energy of analysis frames is not taken into account for linear prediction (LPC) interpolation. Incorporating the frame energy improves the subjective quality of interpolation, but increases the spectral distortion (SD). The main reason for this discrepancy is that the outliers are increased in low energy parts of segments with rapid changes in energy. The energy is most natu...

The autocorrelation function pertaining to spatial distributions of ultrasonic scatterers in soft tissue is believed to contain useful information related to tissue morphology. A simple processing method applied to radio-frequency echo signals estimates this function for a sample having isotropic scattering conditions. It utilizes backscattered echo signals from the sample and echo signals from...

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...

Described is the proposal of application of the GUM uncertainty type A evaluation to measurements with auto-correlated observations. The first step to it is the identification and cleaning of the raw sample data from the regularly variable components. Then formulas for standard deviation of the sample and of the mean value are expressed with the use correction coefficients or the so-called "eff...

Extended Abstract. It is a traditional way in biological, sociological, agricultural and geological studies to partition a geographical area into quadrats and then take a sample of them by a particular sampling design. We study the relevant characteristic of quadrats to estimate a parameter of the population. We suppose that the variable of interest has a positive spatial autocorrelation. Sampl...