جستجوی مقالات مرتبط با کلیدواژه « Forecasting » در نشریات گروه « اقتصاد کشاورزی »

In recent years, the fluctuation in agricultural commodity prices in Iran is increased and thus, accurate forecasting of price change is necessary. In this article, a flexible combined method in modeling monthly prices of beef, lamb and chicken from April 2001 to March 2021, was proposed. In this new method, three different approaches namely simple averaging, discounted and shrinkage methods were effectively used to combine the forecasting outputs of three hybrid methods (MLPANN-GA, MLPANN-PSO and MLPANN-ICA) together. In implementation stage of hybrid methods, based on test and error method, the optimal MLPANN structure was found with 3/2/4–6–1 architectures and the controlling parameters are carefully assigned. The results obtained from three hybrid methods indicate that, based on the RMSE statistical index, the MLPANN-ICA method performs the best when forecasting prices for beef, lamb, and chicken. The outputs of three combination approaches show that the shrinkage method, with a parameter value of K=0.25, achieves the highest prediction accuracy when forecasting prices for these three meats. In summary, the proposed method outperforms the other three hybrid methods overall.

The price forecasting and its changes trend is one of the most important factors in decision making and formulating strategies related to agricultural products. This study aimed at presenting a hybrid data mining model for accurate price forecasting of agricultural products, including nonlinear models of wavelet transform, genetic algorithm, deep neural network and Monte Carlo technique. This proposed model involved a two-stage hybrid model and the base model of nonlinear-nonlinear. In this proposed model, the genetic algorithm for determining the optimal lag of price time series, the wavelet function for the de-noising of price data, the deep neural network for price forecasting, the Monte Carlo method for simulating the most probable price probability and finally, the complex soft calculations for "out-of-sample forecasting with new data set" were used. Results of comparison of the proposed model including "Genetic Algorithm-Wavelet Transform-Deep Neural Network-Monte Carlo", through evaluation criteria, with three competing models of "Genetic Algorithm-Deep Neural Network-Monte Carlo", "Genetic Algorithm-Wavelet Transform-Neural Network-Monte Carlo" and "Genetic Algorithm-Neural Network-Monte Carlo" showed that the proposed model had the better performance in forecasting of future price of saffron compared to the three competing models. Also, the use of deep neural network compared to neural network, the application of wavelet theory for de-noising and also the use of Monte Carlo technique to simulate the predicted prices, increase the forecasting accuracy of future price of saffron. In addition, the use of soft calculations showed that the proposed model had the necessary efficiency and high accuracy for short-term forecasting of the future price of saffron. Therefore, the present study has a good position in achieving the index of maximum accuracy, scenario making of future price trends, sensitivity analysis of components affecting the price and finally, forecasting the future price. Accordingly, the use of the proposed model to forecast the price of agricultural products is recommended.

What is aimed in this research is to determine that are forecasts in years relatively high-price trend and low-price trend affected and smoothed according to the monthly potato and tomato prices? The analysis and forecasting of prices in harvesting seasons of two products are implemented by “t” test and linear regression during 1996-2021. The results have showed significant forecasts for years with normal and high price levels meanwhile the research assumption of forecasting of the price trend has been approved more for potato in a high price level. The results of this research and similar cases can be used to forecast prices for production management of such crops, market regulation and consumers’ welfare of country in different seasons of the year. In case of extra supply with decreasing price level or lack of production with increasing price level one of approaches is to present template and compulsory cultivation in accordance with competitive advantages in provinces and different regions

Present study with the goal of providing food security and self – sufficiency in National Vision Plan, has forecasted demand of selected agricultural products with the usage of Auto Regressive Distributed Lag (ARDL) model at the vision horizon (2025). Results showed that food demand would increase desirable at the basis of food security and consumption pattern reformation provided that vision plan goals had been realized. So that the consumption pattern of Iranian society, under the ideal and desirable alternatives, due to increasing income and decreasing inflation rate, would transform into further increase of protein such as all kinds of meats and eggs and lower consumption of bread. According to the findings, the rank of productions base on the demand in vision horizon (2025) will meat, egg, rice, chicken, wheat and tea respectively.

The importance of economic variables forecasting for policy makers and planners and firms is obvious. Therefore, in recent decades, several models have been developed. In the present study, the Oilcake import quantity predicted by using econometric Model and artificial neural network for 1400-1394. For this purpose, for predict and education network, the data of the 88-1348 and for test the accuracy of forecasts, the data of the 1393-1389 were used. The results showed that forward neural network compared to ARIMA and exponential smoothing method with fewer errors and better performance for predicting of Oilcake import. The results show that the Oilcake imports in 1394 compared to last year, a 32% increase. Therefore, it is necessary to reduce the amount of imported and domestic needs, be protectionist policies. Also, to control of the steep rise in Oilcake import, policy makers and planners would be necessary programs apply, including increased tariffs. The grants for the production and supply companies in the conversion process to oilcake can be used to increase the productivity and competitiveness of these products to be effective.

The main purpose of this study is forecasting the production indexes of sugar beet (land, production and price) in Iran. For this purpose, Models applied to forecast are Auto Regressive (ARMA and ARIMA), Single and Double Exponential Smoothing, Harmonic, Artificial Neural Network and ARCH. The results of Durbin-Watson tests were revealed that land, production and price of sugar beet series are non stochastic. According to the lowest forecasting error criterion, Neural Network, ARMA and ARIMA are the best models for forecast land, production and price of sugar beet series respectively. Also, results of prediction show that all of these indexes will have a increasing trend during the period 2013- 2020, while the increasing trend of land and production are more smoother than price trend.

Forecasting economic variables as a helpful planning tool has got considerable attention in economic literature. There are various methods that could be used for forecasting. The objective of this study is to identify an efficient method for predicting Iran’s leather and skin exports. For this purpose، auto-regressive integrated moving average process (ARIMA)، artificial neural networks (ANN) and hybrid methodology of ANN-ARIMA were compared using time series data from 1971 to 2010. The results showed that hybrid methodology of ANN-ARIMA that decompose the exports time series into its linear and nonlinear forms has better forecasting performance and higher accuracy compare to other methods. In addition، the hybrid methodology as the most accurate method in this research، forecasted the amount of leather and skin export better than the other two methods. However، similar to other two methods it predicts that leather and skin exports would have a downward trend in coming years.

Agriculture as one of the major economic sectors of Iran، has an important role in Gross Domestic Production by providing about 14% of GDP. This study attempts to forecast the value of the agriculture GDP using Periodic Autoregressive model (PAR)، as the new seasonal time series techniques. To address this aim، the quarterly data were collected from March 1988 to July 1989. The collected data was firstly analyzed using periodic unit root test Franses & Paap (2004). The analysis found non-periodic unit root in the seasonal data. Second، periodic seasonal behavior (Boswijk & Franses، 1996) was examined. The results showed that periodic autoregressive model fits agriculture GDP well. This makes an accurate forecast of agriculture GDP possible. Using the estimated model، the future value of quarter agricultural GDP from March 2011 to July 2012was forecasted. With consideration to the fair fit of this model with agricultural GDP، It is recommended to use periodic autoregressive model for the future studies.

Abstract In this study ARIMA and neural network auto-regressive methods for predicting retail product prices of tomatoes were Compared. The data were weekly that included the retail prices of tomatoes during 2009-2010 and gathered from Tehran fruits and vegetables organization. The results showed that non-linear neural network auto-regressive model, in predicting the retail price of tomatoes has a lower error and thus is more efficient than ARIMA.

This study attempts to forecast the export of Iran''s date for the period of 2010-2016، using the econometric methods، ARMA، GARCH، and the methods of computational intelligence، artificial neural networks and genetic algorithms. The data from 1967-2009 were used. The data from 1967- 2005 were used for modeling and the four last years، were used to examine forecast ability. Results indicated that the neural network has the lowest Forecast error comparing other methods followed by Genetic Algorithm. According to the superiority of neural networks in forecasting the Iran''s date export، forecasts made by this، shows the decreasing-increasing trend in the exports of Iran''s date.

Paying attention to the relative stability of prices and forecasting prices could play an important role in controlling the instability of prices and ultimately reduce market risk. Comparison of different methods is important in the forecasting issues. In this study Wholesale and Retail Prices (Weekly Prices) of Rainbow Trout Fish will forecast with contrasting between forecasting power of the ARMA method and Artificial Neural Network method and choice the better one. In this study the Feed-forward network that is one of Back Propagation networks is used. The using data are from first week of farvardin 1388 to last week of shahrivar 1390. Before usage of predict methods، the random or non-randomized nature of the data were examined. Both of Price Series are predictable and non-randomized based on Random tests of Wald-Wolfowitz، Wallis-Moore and Durbin –Watson. Series are stationary in levels based on data stationary test (Dicky-Fuller augmented). Results of Forecasting show that in the model ARMA compared with artificial neural network (ANN) error rate is less based on four criteria forecast accuracy evaluating. Then it has higher power in forecasting the price of Rainbow Trout Fish. In the ANN model 80% of data for training network and 20% for testing network have been used. The results of accuracy equality test of the two methods (MGN) shows the ARMA model is also better than the neural network model in forecasting retail and wholesale prices significantly.

This study introduces the Adaptive Neuro-Fuzzy Inference System (ANFIS) and compares its application in forecasting the three perspectives (1, 2 and 4 year(s)) ahead of Iran’s non-oil export -which the agricultural products made its main share with ARIMA as the most common econometric linear forecasting method during 1959-2008. Results of models performance evaluation showed that the forecasted test data related to ANFIS designed architects have more correspondence with the real data in comparison with the ARIMA forecasted out of sample data. Therefore the non-linear ANFIS model is more efficient than the linear ARIMA model for all considered perspectives of Iran’s non-oil export.

The purpose of doing this study was forecasting liquidity and inflation for the period 1352-1387. After that surveyed unit root and Wallis-Moore nonparametric and Durbin-Watson parametric tests, we used Harmonic; ARMA and ARCH patterns for forecasting and finally the best models were selected for prediction. Results showed that ARMA (2, 1) and GARCH (1, 1) are more efficient models for prediction inflation rate and liquidity, respectively. One of the Iran's economic problems in the past decades is increases liquidity volume that cause raises the price index and therefore, it will create inflation. In the other, agriculture sector is one of the major sectors of economy that influenced by macro-economic policies. Accordingly the second aim of this study is survey impacts of inflation rate and liquidity on agriculture value added. The results of this part showed that there is a direct relation between liquidity, inflation rate and agriculture value added.

The policy makers and economic strategists are trying to model the factors affecting the agricultural sector growth and to use them in the growth forecasting process. Today forecasting is regarded as an important instrument for economic policymakers. There are different methods used to forecast the economic variables. In this paper, the growth rate of Iranian agricultural sector is forecasted and the forecasting accuracies of univariate and multivariate methods are compared. The methods used in this paper include single exponential smoothing with trend, double exponential smoothing with trend, Holt-Winters additive algorithm, Holt-Winters multiplicative algorithm, auto-regressive integrated moving average process, vector auto-regressive approach and artificial neural networks. For univariate models, it was found that the artificial neural networks model, single exponential smoothing with trend and double exponential with trend have marginally better forecasting performance than those of the other methods in this group. Furthermore, for multivariate models the artificial neural networks forecast is more accurate than vector auto-regressive model.