Sensitivity analysis for time lag selection to forecast seasonal time series using neural networks and support vector machines

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2010, Pages

  Cortez, Paulo ^a  

^a UNIVERSITY OF MINHO   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; SUPPORT VECTOR MACHINES; TIME SERIES; TIME SERIES ANALYSIS;

FLEXIBLE LEARNING; FORECASTING METHODS; HOLT-WINTERS; MODEL SELECTION; MULTISTEPS; NEURAL NETWORK AND SUPPORT VECTOR MACHINES; SEASONAL TIME SERIES; TACTICAL DECISIONS; TIME LAG; TIME SERIES FORECASTING;

SENSITIVITY ANALYSIS;

EID: 79959415599     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2010.5596890     Document Type: Conference Paper

References (27)

1. X. Ding, S. Canu, and T. Denoeux, "Neural network based models for forecasting," in Proceedings of ADT'95, 1995.
2. C. Chatfield, The Analysis of Time Series - An Introduction. UK: Chapman and Hall, 1989.
3. P. R. Winters, "Forecasting sales by exponentially weighted moving averages," Management Science, vol. 6, pp. 324-342, 1960.
4. S. Makridakis, S. Weelwright, and R. Hyndman, Forecasting: Methods and Applications, 3rd ed. John Wiley & Sons, New York, USA, 1998.
5. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. NY, USA: Springer-Verlag, 2008.
6. D. Rumelhart, G. Hinton, and R. Williams, "Learning Internal Representations by Error Propagation," in Parallel Distributed Processing: Explorations in the Microstructures of Cognition, D. Rulmelhart and J. McClelland, Eds., vol. 1, MIT Press, Cambridge MA, 1986, pp. 318-362.
7. B. Boser, I. Guyon, and V. Vapnik, "A training algoritm for optimal margin classifiers," in Proceedings of the Fifth Annual Workshop on Computational Learning Theory. San Mateo, CA: Morgan Kaufmann, 1992, pp. 144-152.
8. A. Smola and B. Schölkopf, "A tutorial on support vector regression," Statistics and Computing, vol. 14, pp. 199-222, 2004.
9. A. Lapedes and R. Farber, "Non-Linear Signal Processing Using Neural Networks: Prediction and System Modelling," Los Alamos National Laboratory, USA, Technical Report LA-UR-87-2662, 1987.
10. K. Miiller, A. Smola, G. Ratsch, B. Scholkopf, J. Kohlmorgen, and V. Vapnik, "Predicting time series with support vector machines," in Proceedings of the 7th International Conference on Artificial Neural Networks. Springer, 1997, pp. 999-1004.
11. P. Cortez, M. Rocha, and J. Neves, "Time Series Forecasting by Evolutionary Neural Networks," chapter III, Artificial Neural Networks in Real-Life Applications, Idea Group Publishing, USA, pages 47-70, 2006.
12. W. He, Z. Wang, and H. Jiang, "Model optimizing and feature selecting for support vector regression in time series forecasting," Neurocomputing, vol. 72, no. 1-3, pp. 600-611, 2008.
13. R. Kewley, M. Embrechts, and C. Breneman, "Data Strip Mining for the Virtual Design of Pharmaceuticals with Neural Networks," IEEE Trans Neural Networks, vol. 11, no. 3, pp. 668-679, May 2000.
14. P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis, "Modeling wine preferences by data mining from physicochemical properties," Decision Support Systems, vol. 47, no. 4, pp. 547-553, 2009.
15. R. Hyndman, Time Series Data Library. http://robjhyndman.com/TSDL/, January, 2010.
16. L. Glass and M. Mackey, "Oscillation and chaos in physiological control systems," Science, vol. 197, pp. 287-289, 1977.
17. G. Box and G. Jenkins, Time Series Analysis: Forecasting and Control. Holden Day, San Francisco, USA, 1976.
18. R. Kohavi, "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection," in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Volume 2, Montreal, Quebec, Canada, Morgan Kaufmann, Aug. 1995.
19. P. Cortez, M. Rio, M. Rocha, and P. Sousa, "Internet Traffic Forecasting using Neural Networks," in Proceedings of the 2006 International Joint Conference on Neural Networks (IJCNN 2006). Vancouver, Canada: IEEE, Jul. 2006, pp. 4942-4949.
20. A. Flexer, "Statistical Evaluation of Neural Networks Experiments: Minimum Requirements and Current Practice," in Proceedings of the 13th European Meeting on Cybernetics and Systems Research, vol. 2, Vienna, Austria, 1996, pp. 1005-1008.
21. T. Dietterich, "Ensemble methods in machine learning," in Multiple Classifier Systems, Lecture Notes in Computer Science 1857, J. Kittler and F. Roli, Eds. Springer-Verlag, 2000, pp. 1-15.
22. W. Wang, Z. Xu, W. Lu, and X. Zhang, "Determination of the spread parameter in the Gaussian kernel for classification and regression," Neurocomputing, vol. 55, no. 3, pp. 643-663, 2003.
23. V. Cherkassy and Y. Ma, "Practical Selection of SVM Parameters and Noise Estimation for SVM Regression," Neural Networks, vol. 17, no. 1, pp. 113-126, 2004.
24. R Development Core Team, R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-00-3, http://www.R-project.org, 2009.
25. P. Cortez, "Data Mining with Neural Networks and Support Vector Machines using the R/rminer Tool," in Advances in Data Mining, Proceedings of 10th Industrial Conference on Data Mining, P. Perner, Ed. Berlin, Germany: Springer, Jul. 2010.
26. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. NY, USA: Springer-Verlag, 2001.
27. L. Breiman, "Random forests," Machine learning, vol. 45, no. 1, pp. 5-32, 2001.