NDLI: A comparison of forecasting models using multiple regression and artificial neural networks for the supply and demand of Thai ethanol

Content Provider	IEEE Xplore Digital Library
Author	Homchalee, R. Sessomboon, W.
Copyright Year	2013
Description	Author affiliation: Dept. of Ind. Eng., Khon Kaen Univ., Khon Kaen, Thailand (Homchalee, R.; Sessomboon, W.)
Abstract	This paper presented three types of models for forecasting the supply and demand of Thai ethanol, so called MR, ANN, and MR-ANN models. MR models were formulated using stepwise multiple regression analysis, which were statistically significant. However, MR models provided low performance in forecasting. ANN models were constructed using artificial neural networks, which provided satisfactory results. Moreover, the third type of models was an integration of multiple regression analysis and artificial neural networks. In MR-ANN models, influential factors from stepwise multiple regression, were taken as inputs for artificial neural networks. The integrated models provided a fair results comparing to the first two types of models. In summary, ANN models provided the lowest MAPE and the highest $R^{2}$ indicating that the models were the most appropriate among the three types of models. ANN models are therefore recommended to forecast the supply and demand of Thai ethanol.
Starting Page	963
Ending Page	967
File Size	1449924
Page Count	5
File Format	PDF
ISBN	9781479909865
DOI	10.1109/IEEM.2013.6962554
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-10
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Analytical models artificial neural network Ethanol forecasting Artificial neural networks Production Predictive models Regression analysis multiple regression analysis Forecasting
Content Type	Text
Resource Type	Article

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