| This work presents a new method using time-varying autoregressive modelling for the assessment of heart rate signals stationarity in patients before the onset of ventricular tachyarrhythmias, including comparison with a control group. A general sta-tionarity trend is reported for all subjects, and particularly no signiicant change is observed before an arrhythmic event. Evaluation of the mode...

The classical method of process capability analysis necessarily assumes that collected data are independent; nonetheless, some processes such as biological and chemical processes are autocorrelated and violate the independency assumption. Many processes exhibit a certain degree of correlation and can be treated by autoregressive models among which the autoregressive model of order one (AR (1)) ...

Dynamic factor analysis models with time-varying parameters offer a valuable tool for evaluating multivariate time series data with time-varying dynamics and/or measurement properties. We use the Dynamic Model of Activation proposed by Zautra and colleagues (Zautra, Potter, & Reich, 1997) as a motivating example to construct a dynamic factor model with vector autoregressive relations and time-v...

introduction: agriculture as one of old sectors of economy has been important role in the supply food for peoples and raw materials. globalization causes rapid growth of world trade and reduces information and communications costs. globalization and rapid growth of trade increases the potential benefits of trade for agriculture from various aspects. the potential benefits of trade for agricultu...

Rapid developments of time series models and methods addressing volatility in computational finance and econometrics have been recently reported in the financial literature. The non-linear volatility theory either extends and complements existing time series methodology by introducing more general structures or provides an alternative framework (see Abraham and Thavaneswaran [B. Abraham, A. Tha...

A method for the stabilization of stationary and timevarying autoregressive models is presented. The method is based on the hyperstability constrained LSproblem with nonlinear constraints. The problems are solved iteratively with Gauss-Newton type algorithm that sequentially linearizes the constraints. The proposed method is applied to simulated data in the stationary case and to real EEG data ...

( ) ( ) ( ) t t / L L e 1 1 e t + − = ρ and 1 Y L − + = t t β α , that is, we investigate the nonlinear least squares estimator. Starting with the simplest case 0 = β , we find that ( ) ( ) ( ) 1 e 1 e t + − = α α ρ / which is just a constant so the estimator that minimizes the error sum of squares must be ( ) ( ) ( ) ρ ρ α ˆ / ˆ ˆ − + = 1 1 ln where ρ̂ is the usual regression estimate of (the c...

Parameter estimation of time-varying non-Gaussian autoregressive processes can be a highly nonlinear problem. The problem gets even more difficult if the functional form of the time variation of the process parameters is unknown. In this paper, we address parameter estimation of such processes by particle filtering, where posterior densities are approximated by sets of samples (particles) and p...

This paper suggests a dynamic approach for the term structure of interest rates forecasting using evolving participatory learning fuzzy modeling (ePL). The model includes a time-varying volatility structure in order to predict the yield curve factors. Thus, this framework both comprises an adaptive framework for term structure parameters behavior and deal with the uncertainty related to these f...

In the autoregressive process of first order AR(1), a homogeneous correlated time series ut is recursively constructed as ut = q ut−1 + σ t, using random Gaussian deviates t and fixed values for the correlation coefficient q and for the noise amplitude σ. To model temporally heterogeneous time series, the coefficients qt and σt can be regarded as time-dependent variables by themselves, leading ...