Prediction distribution is a basis for predictive inferences applied in many real world situations. The Bayesian approach under uniform prior employed this paper to derive the prediction Simultaneous Auto-regressive model with multivariate Student-t error distribution. Conditional on set of realized responses, single and future responses have univariate distributions respectively, whose degrees...

‎This paper has two aims. The first is forecasting inflation in Iran using Macroeconomic variables data in Iran (Inflation rate, liquidity, GDP, prices of imported goods and exchange rates) , and the second is comparing the performance of forecasting vector auto regression (VAR), Bayesian Vector-Autoregressive (BVAR), GARCH, time series and neural network models by which Iran's inflation is for...

The advantages of using spatial analysis in annual crop experiments are well documented. There is much less evidence for perennial crops. For the sequence of measurements in perennial crops, apparently, there are no published articles in spatial analysis to date. This paper aimed at the comparison of several models, including auto-regressive, ante-dependence and character process models, in mod...

There is a compelling need to accurately and efficiently compute option values. Existing literature shows that models based on constant stock volatilities have been widely used in option valuation. However, stock volatilities change constantly in real life situations. The introduction of the Auto Regressive Conditional Heteroskedasticity (ARCH) model and subsequently, the Generalized Auto Regre...

This paper presents a method for speech analysis/synthesis/ conversion by using sequential processing. The aims of this method are to improve the quality of synthesized speech and to convert the original speech into another speech of different characteristics. We apply the Kalman Filter for estimating the auto-regressive coefficients of ‘time varying AR model with unknown input (ARUI model)’, w...

Wireless health monitoring schemes are innovative techniques, which effectively remove the disadvantages associated with current wire-based sensing systems, i.e., high installation and upkeep costs. However, recorded data sets may have relative time-delays due to the blockage of sensors or inherent internal clock errors. In this paper, two algorithms are proposed for the synchronization of the ...

Proper models for prediction of time series data can be an advantage in making important decisions. In this study, we tried with the comparison between one of the most useful classic models of economic evaluation, auto-regressive integrated moving average model and one of the most useful artificial intelligence models, adaptive neuro-fuzzy inference system (ANFIS), investigate modeling procedur...