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...

In this paper, the effect of non-normality on sampling plan using Yule’s model (second order auto regressive model {AR (2)}) represented by the Edgeworth series is studied for known $sigma$. The effect of using the normal theory sampling plan in a non-normal situation using Yule’s model is studied by obtaining the distorted errors of the first and second kind. As one will be interes...

stochastic, processes can be stationary or nonstationary. they depend on the magnitude of shocks. in other words, in an auto regressive model of order one, the estimated coefficient is not constant. another finding of this paper is the relation between estimated coefficients and residuals. we also develop a catastrophe and chaos theory for change of roots from stationary to a nonstationary one ...

Forecasting models have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecasters in such unstable environments. Forecasting methods such as Auto Regressive Integrated Moving Average (ARIMA) models and also Artificial Neural Networks (ANNs) need ...

Forecasting models have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecasters in such unstable environments. Forecasting methods such as Auto Regressive Integrated Moving Average (ARIMA) models and also Artificial Neural Networks (ANNs) need ...

In this paper, we have examined 4 models for Great Salt Lake level forecasting: ARMA (Auto-Regression and Moving Average), ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedasticity). Through our empirical data analysis where we div...

In this paper, a new nonlinear wavelet identification structure is proposed for high noise resistive soft sensors. This method uses proposedPolynomial Nonlinear Auto Regressive Exogenous Model, which can be solved with linear Gaussian Least Square Method, alongside the Averaging Wavelet Method (AWM) filter. AWM uses the approximation spaces for analyzing the signals and reduce the noise by a me...

Stochastic, processes can be stationary or nonstationary. They depend on the magnitude of shocks. In other words, in an auto regressive model of order one, the estimated coefficient is not constant. Another finding of this paper is the relation between estimated coefficients and residuals. We also develop a catastrophe and chaos theory for change of roots from stationary to a nonstationary one ...

one of the most significant discussion and challenges propounded in the macroeconomics is the effects of fluctuations of exchange rate on the macroeconomic variables (production, employment, inflation and … etc).in this direction, the important and noticeable point is the factors which lead to fluctuations in the exchange rate which, from amongst these factors as an example, is fluctuations in ...