Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural network models for time series forecasts
Management Science
Forecasting S&P 500 stock index futures with a hybrid AI system
Decision Support Systems
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates
Computers and Operations Research
An adaptively trained neural network
IEEE Transactions on Neural Networks
Dynamic learning rate optimization of the backpropagation algorithm
IEEE Transactions on Neural Networks
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
Application of Radial Basis Function Neural Network for Sales Forecasting
CAR '09 Proceedings of the 2009 International Asia Conference on Informatics in Control, Automation and Robotics
A new architecture selection method based on tabu search for artificial neural networks
Expert Systems with Applications: An International Journal
International Journal of Networking and Virtual Organisations
Applicability of ARIMA Models in Wholesale Vegetable Market: An Investigation
International Journal of Information Systems and Supply Chain Management
Hi-index | 0.01 |
This paper compares the predictive performance of ARIMA, artificial neural network and the linear combination models for forecasting wheat price in Chinese market. Empirical results show that the combined model can improve the forecasting performance significantly in contrast with its counterparts in terms of the error evaluation measurements. However, as far as turning points and profit criterions are concerned, the ANN model is best as well as at capturing a significant number of turning points. The results are conflicting when implementing dissimilar forecasting criteria (the quantitative and the turning points measurements) to evaluate the performance of three models. The ANN model is overall the best model, and can be used as an alternative method to model Chinese future food grain price.