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

Content Provider	Springer Nature Link
Author	Jha, Girish K. Sinha, Kanchan
Copyright Year	2012
Abstract	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.
Starting Page	563
Ending Page	571
Page Count	9
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	24
Issue Number	3-4
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2012-11-15
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	ARIMA Price forecasting Time-delay neural networks Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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