ROBUST V-SUPPORT VECTOR MACHINE AND ITS APPLICATION TO SALE FORECASTING

Authors

  • Hong-Sen Yan Southeast University
  • Xin Tu Southeast University
  • Qi Wu Southeast University

DOI:

https://doi.org/10.23055/ijietap.2018.25.3.1662

Keywords:

Computational Intelligence in IE, Information Systems

Abstract

In view of the low generalization capacity of standard support vector machine for some types of noises existing in time series such as white noise, singularities and biggish magnitude noises, a robust loss function is designed to inhibit (penalize) the above hybrid noises. The structure risk minimization (SRM) based on geometrical interval with a special hyper-plane is theoretically proved. On the basis of that, a new support vector machine called Rv-SVM (robust v-support vector machine) that meets SRM is proposed to deal with sale series. Moreover, by modifying the standard v-SVM formulation, Rv-SVM is characterized by simpler dual optimization and fewer output parameters. With the help of the above, a sale forecasting method based on Rv-SVM and its relevant parameter-choosing algorithm are formulated and applied to car sales forecasting, the convincing results of which definitely confirm the feasibility and validity of the forecasting method. Compared with the standard v-SVM and traditional model, the Rv-SVM method is of better estimating precision and higher generalization capacity.

Author Biographies

Hong-Sen Yan, Southeast University

Hong-Sen Yan received the B.S. degree in automatic control from the Harbin Ship Building Engineering Institute, Harbin, China, in 1982 and the M.S. degree in industrial automation and the Ph.D. degree in automatic control theory and application from the Harbin Institute of Technology, Harbin, in 1989 and 1992, respectively.

From February 1982 to August 1986, he was an Assistant Engineer with the Yangzhou Marine Electronic Instruments Institute, Yangzhou, China. From July 1992 to June 1994, he was a Postdoctoral Fellow with the CIMS Laboratory, Department of Mechanical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China. From July 1994 to March 1998, he was an Associate Professor with the Research Institute of Automation, Southeast University, Nanjing, where he was a Professor from April 1998 to September 2007. Since October 2007, he has been a Professor with the Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, and the Superintendent of the Research Institute of Control and Optimization of Manufacturing Systems, School of Automation, Southeast University. He is the founder of knowledgeable manufacturing and knowledge meshes theory. He has been engaged in or finished the design and development of four computer-integrated manufacturing systems in enterprises with his colleagues. He has authored or coauthored more than 170 research papers in refereed journals such as Applied Artificial Intelligence, Computers and Industrial Engineering, Computers in Industry, Concurrent Engineering: Research & Applications, European Journal of Operational Research, Expert Systems with Applications, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Engineering Management, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Neural Networks, IEEE Transactions on Systems, Man and Cybernetics, Part A, IIE Transactions, Information Sciences, International Journal of Advanced Manufacturing Technology, International Journal of Computer Integrated Manufacturing, International Journal of Industrial Engineering, International Journal of Production Research, Journal of Intelligent and Fuzzy Systems, Journal of Intelligent Manufacturing, Journal of Optimization Theory and Applications, Proceedings of IMechE Part B: Journal of Engineering Manufacture, and Robotics and Computer-Integrated Manufacturing. His research interests include flexible manufacturing systems, computer integrated manufacturing systems (CIMS), agile manufacturing, concurrent engineering, and knowledgeable manufacturing systems.

Dr. Yan is a Senior Member of the Institute of Industrial Engineers, a Director of the Jiangsu Association of Automation in China, and a Senior Member of the Chinese Mechanical Engineering Society. He won the Best Journal Paper Prize for one of the best 200 papers selected from the 2001–2005 years’ papers in the 283 journals sponsored by 105 institutes, societies, institutions, and associations of the China Association for Science and Technology, and also seven other prizes from the State, the ministry, the province, and universities.

Xin Tu, Southeast University

Xin Tu is a Doctor candidate in the School of Automation, Southeast University, Nanjing, Jiangsu, China. He received his M.S degree in Applied Mathematics Department from Guizhou University, Guiyang, China. He has coauthored some research papers in refereed journals such as Applied Artificial Intelligence and Expert Systems with Applications. His current research interests include data mining and demand forecasting.

Qi Wu, Southeast University

Qi Wu received the B.S. degree in automatic control from Anhui University of Technology and Science, Wuhu, China, in 1999, the M.S. degree in virtual manufacturing from Jiangnan University, Wuxi, China, in 2003, and the Ph.D. degree in control theory and engineering from Southeast University, Nanjing, China, in 2009. From February 2009 to January 2011, he was a post-doctor with the School of Mechanical Engineering, Southeast University, Nanjing, China. His research interests include macroeconomics, nonlinear modeling and forecasting. His recent publications have appeared in some peer reviewed journals such as Applied Artificial Intelligence, Engineering Applications of Artificial Intelligence, Expert Systems with Applications, Information Sciences, etc.

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Published

2018-09-27

How to Cite

Yan, H.-S., Tu, X., & Wu, Q. (2018). ROBUST V-SUPPORT VECTOR MACHINE AND ITS APPLICATION TO SALE FORECASTING. International Journal of Industrial Engineering: Theory, Applications and Practice, 25(3). https://doi.org/10.23055/ijietap.2018.25.3.1662

Issue

Vol. 25 No. 3 (2018)

Section

Statistical Analysis

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