Parameters Optimization Using Genetic Algorithms in Support Vector Regression for Sales Volume Forecasting

Author(s)
Fong-Ching Yuan

ABSTRACT

Budgeting planning plays an important role in coordinating activities in organizations. An accurate sales volume forecasting is the key to the entire budgeting process. All of the other parts of the master budget are dependent on the sales volume forecasting in some way. If the sales volume forecasting is sloppily done, then the rest of the budgeting process is largely a waste of time. Therefore, the sales volume forecasting process is a critical one for most businesses, and also a difficult area of management. Most of researches and companies use the statistical methods, regression analysis, or sophisticated computer simulations to analyze the sales volume forecasting. Recently, various prediction Artificial Intelligent (AI) techniques have been proposed in forecasting. Support Vector Regression (SVR) has been applied successfully to solve problems in numerous fields and proved to be a better prediction model. However, the select of appropriate SVR parameters is difficult. Therefore, to improve the accuracy of SVR, a hybrid intelligent support system based on evolutionary computation to solve the difficulties involved with the parameters selection is presented in this research. Genetic Algorithms (GAs) are used to optimize free parameters of SVR. The experimental results indicate that GA-SVR can achieve better forecasting accuracy and performance than traditional SVR and artificial neural network (ANN) prediction models in sales volume forecasting.

Budgeting planning plays an important role in coordinating activities in organizations. An accurate sales volume forecasting is the key to the entire budgeting process. All of the other parts of the master budget are dependent on the sales volume forecasting in some way. If the sales volume forecasting is sloppily done, then the rest of the budgeting process is largely a waste of time. Therefore, the sales volume forecasting process is a critical one for most businesses, and also a difficult area of management. Most of researches and companies use the statistical methods, regression analysis, or sophisticated computer simulations to analyze the sales volume forecasting. Recently, various prediction Artificial Intelligent (AI) techniques have been proposed in forecasting. Support Vector Regression (SVR) has been applied successfully to solve problems in numerous fields and proved to be a better prediction model. However, the select of appropriate SVR parameters is difficult. Therefore, to improve the accuracy of SVR, a hybrid intelligent support system based on evolutionary computation to solve the difficulties involved with the parameters selection is presented in this research. Genetic Algorithms (GAs) are used to optimize free parameters of SVR. The experimental results indicate that GA-SVR can achieve better forecasting accuracy and performance than traditional SVR and artificial neural network (ANN) prediction models in sales volume forecasting.

Cite this paper

F. Yuan, "Parameters Optimization Using Genetic Algorithms in Support Vector Regression for Sales Volume Forecasting,"*Applied Mathematics*, Vol. 3 No. 10, 2012, pp. 1480-1486. doi: 10.4236/am.2012.330207.

F. Yuan, "Parameters Optimization Using Genetic Algorithms in Support Vector Regression for Sales Volume Forecasting,"

References

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[2] J. Stack, “A Passion for Forecasting,” Springfield Manufacturing, Inc., Springfield, 1997, pp.37-38.

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[6] M. Lawrence and M. O’Connor, “Sales Forecasting Updates: How Good Are They in Practice?” International Journal of Forecasting, Vol. 16, No. 3, 2000, pp. 369-382. doi:10.1016/S0169-2070(00)00059-5

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[13] E. H. T. Francis and L. Cao, “Application of Support Vector Machines in Financial Time Series Forecasting,” International Journal of Management Science, Vol. 29, No. 4, 2001, pp. 309-317

[14] B. Yukun, R. Zhang and S. F. Crone, “Fuzzy Support Vector Machines Regression for Business Forecasting: An Application,” Fuzzy Systems and Knowledge Discovery, Vol. 4223, 2006, pp. 1313-1317.

[15] L. Yu, S. Wang and J. Cao, “A Modified Least Squares Support Vector Machine Classifier with Application to Credit Risk Analysis,” International Journal of Information Technology & Decision Making, Vol. 8, No. 4, 2009, pp. 697-710. doi:10.1142/S0219622009003600

[16] C. H. Zheng, G. W. Zheng and L. C. Jiao, “Heuristic Genetic Algorithm-Based Support Vector Classifier for Recognition of Remote Sensing Images,” In: Advance in Neural Networks, Lecture Notes in Computer Science, Springer-Verlag, New York, Vol. 3173, 2004, pp. 629-635.

[17] K. Duan, S. S. Keerthi, A. N. Poo, “Evaluation of Simple Performance Measures for Tuning SVM Hyperparameters,” Neurocomputing, Vol. 51, No. 1-4, 2003, pp. 41-59. doi:10.1016/S0925-2312(02)00601-X

[18] M. Kaya, “MOGAMOD: Multi-Objective Genetic Algorithm for Motif Discovery,” Expert Systems with Applications, Vol. 36, No. 2, 2007, pp. 1039-1047. doi:10.1016/j.eswa.2007.11.008

[19] X. G. Chen, “Railway Passenger Volume Forecasting Based on Support Machine and Genetic Algorithm,” 2009 ETP International Conference on Future Computer and Communication, 6-7 June 2009, Wuhan, pp. 282-284. doi:10.1109/FCC.2009.81

[20] C. H. Wu, G. H. Tzeng and R. H. Lin, “A Novel Hybrid Genetic Algorithm for Kernel Function and Parameter Optimization in Support Vector Regression,” Expert Systems with Applications, Vol. 36, No. 3, 2009, pp. 4725-4735. doi:10.1016/j.eswa.2008.06.046

[21] J. H. Holland, “Adaptation in Natural and Artificial Systems,” University of Michigan, MIT Press, Cambridge, 1975.

[22] P. F. Pai and W.C. Hong, “Forecasting Regional Electric Load Based on Recurrent Support Vector Machines with Genetic Algorithms,” Electric Power Systems Research, Vol. 74, No. 3, 2005, pp. 417-425. doi:10.1016/j.epsr.2005.01.006

[1] R. H. Garrison and E. W. Noreen, “Managerial Accounting 10/e,” McGraw-Hill, New York, 2003.

[2] J. Stack, “A Passion for Forecasting,” Springfield Manufacturing, Inc., Springfield, 1997, pp.37-38.

[3] C. D. Lewis, “Industrial and Business Forecasting Methods,” Butterworths, London, 1982.

[4] R. Fildes, R. Hastings, “The Organization and Improvement of Market Forecasting,” Journal of Operation Research Society, Vol. 45, No. 1, 1994, pp. 1-16.

[5] C. W. J. Granger, “Can We Improve the Perceived Quality of Economic Forecasts?” Journal Applied Econometric, Vol. 11, No. 5, 1996, pp. 455-473.

[6] M. Lawrence and M. O’Connor, “Sales Forecasting Updates: How Good Are They in Practice?” International Journal of Forecasting, Vol. 16, No. 3, 2000, pp. 369-382. doi:10.1016/S0169-2070(00)00059-5

[7] Y. K. Bao, Y. S. Lu and J. L. Zhang, “Forecasting Stock Price by SVMs Regression,” Lecture Notes in Artificial Intelligence, Vol. 3192, 2004, pp. 295-303.

[8] R. J. Kuo and K. C. Xue, “A Decision Support System for Sales Forecasting through Fuzzy Neural Networks with Asymmetric Fuzzy Weights,” Decisions Support Systems, Vol. 24, No. 2, 1998, pp. 105-126. doi:10.1016/S0167-9236(98)00067-0

[9] R. Capparuccia, R. De. Leone, E. Marchitto, “Integrating Support Vector Machines and Neural Networks,” Neural Networks, Vol. 20, No. 5, 2007, pp. 590-597. doi:10.1016/j.neunet.2006.12.003

[10] V. N. Vapnik, “The Nature of Statistical Learning Theory,” Springer, New York, 1995.

[11] V. Vapnik, S. Golowich and A. Smola, “Support Vector Method for Function Approximation, Regression Estimation and Signal Processing,” In: M. Mozer, M. Jordan & T. Petsche, Eds., Advance in Neural Information Processing System, MIT Press, Cambridge, 1997, pp.281-287.

[12] K. R. Muller, A. J. Smola, G. Ratsch, B. Scholkopf, J. Kohlmorgen and V. Vapnik, “Prediction Time Series with Support Vector Machines,” Lecture Notes in Computer Science, Vol. 1327, 1997, pp. 999-1004. doi:10.1007/BFb0020283

[13] E. H. T. Francis and L. Cao, “Application of Support Vector Machines in Financial Time Series Forecasting,” International Journal of Management Science, Vol. 29, No. 4, 2001, pp. 309-317

[14] B. Yukun, R. Zhang and S. F. Crone, “Fuzzy Support Vector Machines Regression for Business Forecasting: An Application,” Fuzzy Systems and Knowledge Discovery, Vol. 4223, 2006, pp. 1313-1317.

[15] L. Yu, S. Wang and J. Cao, “A Modified Least Squares Support Vector Machine Classifier with Application to Credit Risk Analysis,” International Journal of Information Technology & Decision Making, Vol. 8, No. 4, 2009, pp. 697-710. doi:10.1142/S0219622009003600

[16] C. H. Zheng, G. W. Zheng and L. C. Jiao, “Heuristic Genetic Algorithm-Based Support Vector Classifier for Recognition of Remote Sensing Images,” In: Advance in Neural Networks, Lecture Notes in Computer Science, Springer-Verlag, New York, Vol. 3173, 2004, pp. 629-635.

[17] K. Duan, S. S. Keerthi, A. N. Poo, “Evaluation of Simple Performance Measures for Tuning SVM Hyperparameters,” Neurocomputing, Vol. 51, No. 1-4, 2003, pp. 41-59. doi:10.1016/S0925-2312(02)00601-X

[18] M. Kaya, “MOGAMOD: Multi-Objective Genetic Algorithm for Motif Discovery,” Expert Systems with Applications, Vol. 36, No. 2, 2007, pp. 1039-1047. doi:10.1016/j.eswa.2007.11.008

[19] X. G. Chen, “Railway Passenger Volume Forecasting Based on Support Machine and Genetic Algorithm,” 2009 ETP International Conference on Future Computer and Communication, 6-7 June 2009, Wuhan, pp. 282-284. doi:10.1109/FCC.2009.81

[20] C. H. Wu, G. H. Tzeng and R. H. Lin, “A Novel Hybrid Genetic Algorithm for Kernel Function and Parameter Optimization in Support Vector Regression,” Expert Systems with Applications, Vol. 36, No. 3, 2009, pp. 4725-4735. doi:10.1016/j.eswa.2008.06.046

[21] J. H. Holland, “Adaptation in Natural and Artificial Systems,” University of Michigan, MIT Press, Cambridge, 1975.

[22] P. F. Pai and W.C. Hong, “Forecasting Regional Electric Load Based on Recurrent Support Vector Machines with Genetic Algorithms,” Electric Power Systems Research, Vol. 74, No. 3, 2005, pp. 417-425. doi:10.1016/j.epsr.2005.01.006