Abstract
In this article, the quantity of grapes sold in one fruit shop of an interlocking fruit supermarket is forecasted by the method of support vector machine (SVM) based on deficient data. Since SVMs have a lot advantages such as great generalization performance and guarantying global minimum for given training data, it is believed that support vector regression will perform well for forecasting sales of grapes. In order to improve forecasting precision (FP), this article quantifies the factors affecting the sales forecast of grapes such as weather and weekend or weekday, results are suitable for real situations. In this article, we apply ε-SVR and LS-SVR to forecast sales of three varieties of grapes. Moreover, the artificial neural network (ANN) and decision tree (DT) are used as contrast and numerical experiments show that forecasting systems with SVMs is better than ANN and DT to forecast the daily sales of grapes overall.
This paper is supported by the China Agricultural Research System (CARS-30).
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References
Smola, A.: Regression estimation with support vector learning machines. Master’s thesis, Technische University at Munchen (1996)
Chakraborty, K., Mehrotra, K., Mohan, C.: Forecasting the behavior of multivariate time series using neural networks. Neural Networks 5(6), 961–970 (1992)
Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20, 273–297 (1995)
Doumpos, M.: An Experimental Comparison of Some Efficient Approaches for Training Support Vector Machines. Operational Research 4(1), 45–56 (2004)
Tang, H., Qu, L.: Fault diagnosis of engine based on support vector machine. Journal of Xi’an Jiaotong University 9, 1124–1126 (2007) (in Chinese)
Wu, J., Dong, T.: SVM applied to modeling of cancer date. Science Technology and Engineering 20(7), 5363–5365 (2007)
Pelckmans, K., Suykens, J.A.K.: LS-SVMlab Toolbox User’s Guide. Katholieke Universiteit Leuven. ESAT-SCD-SISTA Technical Report, 02-145 (2003)
Wu, Q., Yan, H.-S., Yang, H.-B.: A Forecasting Model Based Support Vector Machine and Particle Swarm Optimization. Power Electronics and Intelligent Transportation System (2008)
Roy, A., Samanta, G.P.: Inventory Model with Two Rates of Production for Deteriorating Items with Permissible Delay in Payment. International Journal of Systems Science 42, 1375–1386 (2011)
Gunn, S.R.: Support Vector Machines for Classification and Regression. ISIS Technical Report, University of Southampton, Department of Electronics and Computer Science (1998)
Vapnik, V.N.: The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (2000)
Du, X.F., Leung, S.C.H.: Demand forecasting of perishable farm products using support vector machine. International Journal of Systems Science, 1–12 (2011)
Xu, X.-H., Zhang, H.: Forecasting Demand of Short Life Cycle Products by SVM. In: International Conference on Management Science & Engineering, vol. 9, pp. 10–12 (2008)
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Wen, Q., Mu, W., Sun, L., Hua, S., Zhou, Z. (2014). Daily Sales Forecasting for Grapes by Support Vector Machine. In: Li, D., Chen, Y. (eds) Computer and Computing Technologies in Agriculture VII. CCTA 2013. IFIP Advances in Information and Communication Technology, vol 420. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54341-8_37
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DOI: https://doi.org/10.1007/978-3-642-54341-8_37
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