Time-delay neural networks for time series prediction: an application to the monthly wholesale price of oilseeds in India

• Published in: Neural computing & applications 2014-03, Vol.24 (3-4), p.563-571
• Main Authors: Jha, Girish K., Sinha, Kanchan
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Connectionism. Neural networks; Data Mining and Knowledge Discovery; Exact sciences and technology; Image Processing and Computer Vision; Inference from stochastic processes; time series analysis; Mathematics; Operational research and scientific management; Operational research. Management science; Original Article; Planning. Forecasting; Probability and statistics; Probability and Statistics in Computer Science; Sciences and techniques of general use; Statistics
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-012-1264-z

Agricultural price forecasting is one of the challenging areas of time series forecasting. The feed-forward time-delay neural network (TDNN) is one of the promising and potential methods for time series prediction. However, empirical evaluations of TDNN with autoregressive integrated moving average (ARIMA) model often yield mixed results in terms of the superiority in forecasting performance. In this paper, the price forecasting capabilities of TDNN model, which can model nonlinear relationship, are compared with ARIMA model using monthly wholesale price series of oilseed crops traded in different markets in India. Most earlier studies of forecast accuracy for TDNN versus ARIMA do not consider pretesting for nonlinearity. This study shows that the nonlinearity test of price series provides reliable guide to post-sample forecast accuracy for neural network model. The TDNN model in general provides better forecast accuracy in terms of conventional root mean square error values as compared to ARIMA model for nonlinear patterns. The study also reveals that the neural network models have clear advantage over linear models for predicting the direction of monthly price change for different series. Such direction of change forecasts is particularly important in economics for capturing the business cycle movements relating to the turning points.