Application of SVM combined with mackov chain for inventory prediction in supply chain

2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008

Volumn , Issue , 2008, Pages

  Wang, Jianzhou ^a   Sun, Donghuai ^a   Zhu, Wenjin ^a   Lu, Haiyan ^b  

^a LANZHOU UNIVERSITY   (China)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

Markov chain; Predict; Supply chain; SVM

Indexed keywords

ARTIFICIAL INTELLIGENCE; ELECTRIC LOAD FORECASTING; FORECASTING; MARKOV PROCESSES; MOBILE COMPUTING; SUPPLY CHAIN MANAGEMENT; SUPPLY CHAINS; WIRELESS NETWORKS; WIRELESS TELECOMMUNICATION SYSTEMS;

COMBINED MODELS; DISCRETE STATES; FORECASTING MODELS; MARKOV CHAIN; PREDICT; RANDOM FACTORS; STATISTICAL DATUMS; STATISTICAL LEARNING THEORIES; SUPPORT VECTORS; SVM; SVM MODELS;

SUPPORT VECTOR MACHINES;

EID: 58049122826     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WiCom.2008.1543     Document Type: Conference Paper

References (11)

1. Anna Nagurney: Supply Chain. Network. Economics: Dynamics of Prices, Flows, and Profits, Edward Elgar Publishing, 2006.
2. M. Christopher, Logistic and Supply Chain Management, Pitman Publishing, London, 1992.
3. M.P. Baganha, M. Cohen, The stabilizing elect of inventory in supply chains, Operations Research 46 (3) (1998) S72-S73.
4. E. Porteus, Stochastic inventory theory, in: D.P. Heyman, M.J. Sobel (Eds.), Handbooks of Operations Research, North-Holland, Amsterdam, 1990.
5. V.N. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
6. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York, 2001.
7. S.P. Meyn, R.L. Tweedie, Markov chains and stochastic stability, Springer-Verlag, 1993.
8. J.T. Kwok, I.W. Tsang, Linear dependency between. ε and the input noise in ε-support vector regression, IEEE Trans. Neural Networks 14 (3) (2003) 544-553.
9. W. He, Z. Wang, Optimized local kernel machines for fast time series forecasting , in: Proceedings of the Third International Conference on Natural Computation, Haikou, China, 2007,pp. 620-627.
10. Wenwu He, Zhizhong Wang, Model optimizing and feature selectingfor support vector regression in time series forecasting, Elsevier,2008.
11. Guo-Dong Li , Daisuke Yamaguchi, A GM(1,1)-Markov chain, combined model with an application to predict the number of Chinese international airlines, Technological Forecasting & Social Change 74 (2007)1465-1481