Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
Forecasting of Grape Juice Flavor by Using Support Vector Regression

Authors: Ren-Jieh Kuo, Chun-Shou Huang

Abstract:

The research of juice flavor forecasting has become more important in China. Due to the fast economic growth in China, many different kinds of juices have been introduced to the market. If a beverage company can understand their customers’ preference well, the juice can be served more attractive. Thus, this study intends to introducing the basic theory and computing process of grapes juice flavor forecasting based on support vector regression (SVR). Applying SVR, BPN, and LR to forecast the flavor of grapes juice in real data shows that SVR is more suitable and effective at predicting performance.

Keywords: Flavor forecasting, artificial neural networks, support vector regression, grape juice flavor.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1105219

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1761

References:


[1] A. Vellido, P. J. Lisboa, and J. Vaughan, “Neural networks in business: A survey of applications (1992–1998),” Expert Systems with Applications, vol. 17, no. 1, 51-70, 1999.
[2] G. S. Atsalakis, and K. P. Valavanis, “Surveying stock market forecasting techniques–Part II: Soft computing methods,” Expert Systems with Applications, vol. 36, no. 3, 5932–5941, 2009.
[3] L. Cao, “Support vector machines experts for time series forecasting,” Neurocomputing, 51, 321-339, Apr. 2003
[4] L. Cao, and F. E. Tay, “Financial forecasting using support vector machines,” Neural Computing & Applications, 10(2), 184-192, 2001.
[5] L.J. Cao, and F. E. H. Tay, “Support vector machine with adaptive parameters in financial time series forecasting,” Neural Networks, IEEE Transactions on, vol. 14, no. 6, 1506-1518, 2003.
[6] F. E. Tay, and L. Cao, “Application of support vector machines in financial time series forecasting,” Omega, vol. 29, no. 4, 309-317, 2001.
[7] V. N. Vapnik, V. N. “An overview of statistical learning theory,” Neural Networks, IEEE Transactions on, vol. 10, no. 5, 988-999, 1999.
[8] C. J. Lu, and Y. W. Wang, “Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting,” International Journal of Production Economics, vol. 128, no. 2, 603-613, 2010.
[9] K. Y. Chen, and C. H. Wang, “Support vector regression with genetic algorithms in forecasting tourism demand,” Tourism Management, vol. 28, no. 1, 215-226, 2007.
[10] E. E. Elattar, J. Goulermas, and Q. H. Wu, Q. H. “Electric load forecasting based on locally weighted support vector regression. Systems, Man, and Cybernetics,” Part C: Applications and Reviews, IEEE Transactions on, vol. 40, no.4, 438-447, 2010.
[11] W. C Hong, “Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm,” Neurocomputing, vol. 74, no. 12, 2096-2107, 2011.
[12] W. C. Hong, Y. Dong, F. Zheng, and C. Y. Lai, “Forecasting urban traffic flow by SVR with continuous ACO,” Applied Mathematical Modelling, vol. 35, no. 3, 1282-1291, 2011.
[13] W. C. Hong, Y. Dong, F. Zheng, and S. Y. Wei, “Hybrid evolutionary algorithms in a SVR traffic flow forecasting model,” Applied Mathematics and Computation, 217(15), 6733-6747, 2011.
[14] C. J. Lu, T. S. Lee, and C. C. Chiu, “Financial time series forecasting using independent component analysis and support vector regression,” Decision Support Systems, vol. 47, no. 2, 115-125,2009.
[15] K. J. Kim, “Financial time series forecasting using support vector machines,” Neurocomputing, vol. 55, no.1, 307-319,2003.
[16] X. Liang, H. Zhang, J. Xiao, and Y. Chen, “Improving option price forecasts with neural networks and support vector regressions.,” Neurocomputing, vol. 72, no. 13, 3055-3065, 2009.
[17] M. Castro-Neto, Y. S. Jeong, M. K. Jeong, and L. D. Han, “Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions,” Expert Systems with Applications, vol. 36, no. 3, 6164-6173, 2003.
[18] P. F. Pai, S. L. Yang, and P. T. Chang, “Forecasting output of integrated circuit industry by support vector regression models with marriage honey-bees optimization algorithms,” Expert Systems with Applications, vol. 36, no. 7, 10746-10751, 2009.
[19] V. Cherkassky, and Y. Ma, Practical selection of SVM parameters and noise estimation for SVM regression. Neural networks, vol. 17, no. 1, 113-126, 2004.
[20] G. Zhang, B. Eddy Patuwo, B., & Y. Hu, “Forecasting with artificial neural networks: The state of the art,” International Journal of Forecasting, vol. 14, no. 1, 35-62, 1998.
[21] Y. Chauvin, and D. E. Rumelhart, “Back-propagation: Theory, architectures, and applications,” NJ: Lawrence Erlbaum, Hillsdale, 1995, pp. 1-35.
[22] S. Haykin, “Neural networks: A comprehensive foundation,” Prentice Hall PTR upper Saddle River, 1994.
[23] P. L. Bartlett, “The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network,” Information Theory, IEEE Transactions on, vol. 44, no. 2, 525-536, 1998
[24] Y. Lee, S. H. Oh, M. W. Kim, “An analysis of premature saturation in back propagation learning,” Neural networks, vol. 6, no. 5, 719-728,1993.
[25] S. F. Crone, J. Guajardo, and R. Weber, “The impact of preprocessing on support vector regression and neural networks in time series prediction,” Paper presented at the DMIN, 2006.
[26] S. F. Crone, S. Lessmann, and R. Stahlbock, “The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing,” European Journal of Operational Research, vol. 173, no. 3, 781-800, 2006.
[27] Z. Tang, and P. A. Fishwick, “Feed-forward neural nets as models for time series forecasting,” ORSA Journal on Computing, vol. 5, no. 4, 374-385,1993.