Neural network models were compared to traditional forecasting methods in forecasting the quarterly and monthly farm price of hogs. A quarterly neural network model forecasted poorly in comparison to a quarterly econometric model. A monthly neural network model outperformed a monthly ARIMA model with respect to the mean square error criterion and performed similarly to the ARIMA model with respect to turning point accuracy. The more positive results of the monthly neural network model in comparison to the quarterly neural network model may be due to nonlinearities in the monthly data which are not in the quarterly data
O estudo comparou o desempenho preditivo dos modelos de previsão de redes neurais e de suavização ex...
The performance and economic value of public outlook forecasts has been of continuing interest to ag...
The development of machine learning research has provided statistical innovations and further develo...
Neural network models were compared to traditional forecasting methods in forecasting the quarterly ...
Forecasts of food prices are intended to be useful for farmers, policymakers and agribusiness indust...
Not AvailableAgricultural price forecasting is one of the challenging areas of time series forecasti...
The lack of study among the economic forecasting literature that can empirically proves the hypothes...
In this study the prediction capabilities of Artificial Neural Networks and typical econometric meth...
Forecasting has been very important in decision making at all levels and sectors of the economy. In...
AbstractIt is well known that short-term market price forecasting has been a difficult problem for a...
In general, the agricultural producing sector is affected by the diversity in supply, mostly from sm...
The value of neural network models in forecasting economic time series has been established for Nort...
The goal of this paper is to compare and analyze the forecasting performance of two artificial neura...
This study compares the forecasting ability of an econometric and neural-network model of fresh toma...
Human forecasting capacity is still very limited. In spite of the extreme efforts of specialists in ...
O estudo comparou o desempenho preditivo dos modelos de previsão de redes neurais e de suavização ex...
The performance and economic value of public outlook forecasts has been of continuing interest to ag...
The development of machine learning research has provided statistical innovations and further develo...
Neural network models were compared to traditional forecasting methods in forecasting the quarterly ...
Forecasts of food prices are intended to be useful for farmers, policymakers and agribusiness indust...
Not AvailableAgricultural price forecasting is one of the challenging areas of time series forecasti...
The lack of study among the economic forecasting literature that can empirically proves the hypothes...
In this study the prediction capabilities of Artificial Neural Networks and typical econometric meth...
Forecasting has been very important in decision making at all levels and sectors of the economy. In...
AbstractIt is well known that short-term market price forecasting has been a difficult problem for a...
In general, the agricultural producing sector is affected by the diversity in supply, mostly from sm...
The value of neural network models in forecasting economic time series has been established for Nort...
The goal of this paper is to compare and analyze the forecasting performance of two artificial neura...
This study compares the forecasting ability of an econometric and neural-network model of fresh toma...
Human forecasting capacity is still very limited. In spite of the extreme efforts of specialists in ...
O estudo comparou o desempenho preditivo dos modelos de previsão de redes neurais e de suavização ex...
The performance and economic value of public outlook forecasts has been of continuing interest to ag...
The development of machine learning research has provided statistical innovations and further develo...