Research Article | OPEN ACCESS
A Hybrid Fresh Apple Export Volume Forecasting Model Based on Time Series and Artificial Neural Network
Lihua Yang and Baolin Li
School of Economics and Management, Hubei University of Automotive Technology, Shiyan 442002, China
Advance Journal of Food Science and Technology 2015 12:966-970
Received: November ‎30, ‎2014 | Accepted: January ‎8, ‎2015 | Published: April 25, 2015
Abstract
Export volume forecasting of fresh fruits is a complex task due to the large number of factors affecting the demand. In order to guide the fruit growers’ sales, decreasing the cultivating cost and increasing their incomes, a hybrid fresh apple export volume forecasting model is proposed. Using the actual data of fresh apple export volume, the Seasonal Decomposition (SD) model of time series and Radial Basis Function (RBF) model of artificial neural network are built. The predictive results are compared among the three forecasting model based on the criterion of Mean Absolute Percentage Error (MAPE). The result indicates that the proposed combined forecasting model is effective because it can improve the prediction accuracy of fresh apple export volumes.
Keywords:
Artificial neural network, forecasting, fresh apple, time series,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2042-4876
ISSN (Print): 2042-4868 |
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