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Showing 4 results for Arima

Hamid Mohammadi, Seyed Nemat-Allah Moosavi, Jafar Azizi,
Volume 16, Issue 45 (4-2008)
Abstract

This study is carried out with the objective of forecasting nominal and real prices of some agricultural products such as onions, potatoes and tomatoes. After the analysis of stationarity of the associated time-series, the randomness of the variables is studied using the non-parametric Wald-Wulfowitz and the parametric Durbin-Watson tests. The results show that all the nominal price series of the said products and the real price series of potatoes were identified as non-random and forecastable time-series. The time-horizon for this study is 1350-1384. Models used for the forecasting in this analysis were AR, MA, ARIMA, Single Exponential Smoothing, Double Exponential Smoothing, Harmonic Analysis, ARCH and the Artificial Neuro-network. Amongst the four forecasting series based on the minimum-error criterion, the AR and MA models forecasted the nominal price series of onions and potatoes much better. The Harmonic model forecasted the nominal price of tomatoes with minimal error. The forecasted values for the real price of potatoes using the ARCH model produced the minimum errors. Forecasting error for the nominal price series of potatoes was less than its real price series. The forecasting values for the year 1385 and 1386 were shown to be mere than the similar values for 1384, with the exception of the price for potatoes.
Seyed Ebrahim Dashty, Hamid Mohammadi,
Volume 18, Issue 55 (10-2010)
Abstract

The aim of this study was to choose forecasting model for nominal and real price of beef, sheep meat, Milk, and Wool. Initially the stationary of the series was tested, then in order to investigate whether series are stochastic, nonparametric test of Vald-Wulfowitz was applied. The study period covers 1346-1384. Based on the above tests results, all of the selected nominal and real prices were recognized to be predictable. The models applied to forecast are ARIMA, and Artificial Neural Network (ANN). The findings indicated the relative superiority of ARIMA in comparison with ANN in predicting nominal prices of selected products. However, In the case of real prices ANN showed a comparative superiority. It was also found that in the case of nominal series increase in the forecast period lead to increased forecast error.
Ahmad Jafarnejad, Mohsen Soleymani,
Volume 19, Issue 57 (4-2011)
Abstract

Health sector and its infrastructure needs in both software and hardware sector has always been emphasized. Among importance of the medical equipment and items in the health system of the country is not covered on any one. Organizations and companies active in this sector should be able to take correct decisions with regard to information in the volatile business environment today on time. Thus, estimating demand in future periods seems vital. There are various methods and tools for forecasting demand that each have advantages and disadvantage its own special. In this paper, using a multilayer neural network with two hidden layers that has been learned with genetic algorithm as the learning algorithm, the comparative system with Common method used in the prediction (Box - Jenkins Method) with model ARIMA (2,1,1) has been presented for the forecasting demand CT-Scan set, that According to the measure of the accuracy of models, the mean squared error (MSE), the neural network model shown of the more effectiveness and efficiency as compared to ARIMA method according to the data and information in forecasting demand CT-Scan set.
Hamid Mohammadi, Zakaria Farajzadeh,
Volume 19, Issue 59 (10-2011)
Abstract

The aim of this study is to introduce appropriate models for forecasting the gas oil prices in Singapore market which influence the gas-oil price in the Middle East. The used data are on weekly basis and covering the period of (1997-2010).The forecasts were made for 10, 20 and 30 percent ages of the data, separately. The models have been employed for prosecutions included four models of Neural Network and ARIMA model. The 4 selected Neural Networks are Feed-Forward Back Propagation, Cascade Back Propagation, Elman Back Propagation and Generalized Regression. The training functions are also Levenberg-Marquardt and Quasi-Newton BFG. The results that the least forecasting error belongs to the network in the Levenberg-Marquardt training function has been used. The results also reveal that for forecasts of 20 and 30 percent of data, Elman Back Propagation and for the 10 percent of the data Feed-Forward Back Propagation networks have the least forecast error. Moreover, the findings also reveal that Generalized Regression network and ARIMA have the largest forecasting error as compared to the other models. However, the findings of Diebold-Mariano statistics showed no statistically significant difference among the networks with the least forecast error, in respect to forecasting accuracy for the gas-oil price using a combination of 80 percent for training and 20 percent of day a for forecasting, as compared to the data combination, how less forecasting error. Finally, forecasting error of in the case of the best model is around 2 percent.

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فصلنامه پژوهشها و سیاستهای اقتصادی Journal of Economic Research and Policies
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