Intelligent system for time series classification using support vector machines applied to supply-chain

Expert Systems with Applications

Volumn 39, Issue 12, 2012, Pages 10590-10599

  Turrado García, Fernando ^a   García Villalba, Luis Javier ^a   Portela, Javier ^a  

^a UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

Author keywords

ARIMA; Supply chain; Support vector machines; Time series

Indexed keywords

ARIMA; ARIMA MODELS; ARTIFICIAL INTELLIGENCE TECHNIQUES; EXPONENTIAL SMOOTHING; FORECASTING MODELS; HOLT-WINTERS; KEY FACTORS; NEW MODEL; OPTIMAL STRATEGIES; STORAGE COSTS; TIME SERIES CLASSIFICATIONS; VIRTUAL MODELS;

INDUSTRY; INTELLIGENT SYSTEMS; SUPPLY CHAINS; SUPPORT VECTOR MACHINES;

TIME SERIES;

EID: 84861191888     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.02.137     Document Type: Article

References (19)

1. G.E.P. Box, G.M. Jenkins, and G.C. Reinsel Time series analysis: forecasting and control 4th ed. 1994 Prentice-Hall Englewood Cliffs, New Jersey
2. W. Cai, X. Chen, and X. Zhao Supply chain performance comprehensive evaluation based on support vector machine, modeling risk management for resources and environment in China Computational Risk Management 3 2011 225 232
3. Chang, C. C.; and Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2:27:1-27:27. Software available at .
4. C. Chatfield The holt-winters forecasting procedure Journal of the Royal Statistical Society. Series C (Applied Statistics) 27 3 1978 264 279
5. C. Chatfield, and M. Yar Holt-winters forecasting: Some practical issues Journal of the Royal Statistical Society. Series D (The Statistician) 37 2 1988 129 140
6. F. Chen, Z. Drezner, J.K. Ryan, and D. Simchi-Levi Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, lead times, and information Management Science 46 3 2000 436 443
7. P. Danese, and P. Romano Supply chain integration and efficiency performance: a study on the interactions between customer and supplier integration Supply Chain Management: An International Journal 16 4 2011 220 230
8. V.S. Ediger, and S. Akar ARIMA forecasting of primary energy demand by fuel in Turkey Energy Policy 35 3 2007 1701 1708
9. Forrester, J. W. (1999). Industrial dynamics. Boston, EUA: Pegasus Communications.
10. R.J. Kuo, and Y.S. Han A hybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem - A case study on supply chain model Applied Mathematical Modeling 35 8 2011 3905 3917
11. H.L. Lee, V. Padmanabhan, and S. Whang Information distortion in a supply chain: the bullwhip effect Management Science 43 1997 546 558
12. G.M. Ljung, and G.E.P. Box On a measure of a lack of fit in time series models Biometrika 65 1978 297 303
13. S.H. Min, J. Lee, and I. Han Hybrid genetic algorithms and support vector machines for bankruptcy prediction Expert Systems with Applications 31 3 2006 652 660
14. R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: http://www.R-project.org.
15. M. Shukla, and S. Jharkharia ARIMA models to forecast demand in fresh supply chains International Journal of Operational Research 11 1 2011 1 18
16. J. Shahrabi, S.S. Mousavi, and M. Heydar Supply chain demand forecasting; a comparison of machine learning techniques and traditional methods Journal of Applied Sciences 9 2009 521 527
17. B. Schölkopf, K. Sung, C. Burges, F. Girosi, P. Niyogi, and T. Poggio Comparing support vector machines with Gaussian kernels to radial basis function classifiers IEEE Transactions on Signal Processing 45 1997 2758 2765
18. B. Schölkopf, A. Smola, R. Williamson, and P.L. Bartlett New support vector algorithms Neural Computation 12 2000 1207 1245
19. C.-C. Wang A comparison study between fuzzy time series model and ARIMA model for forecasting Taiwan export Expert Systems with Applications 38 8 2011 9296 9304