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Mathematical Problems in Engineering
Volume 2010 (2010), Article ID 513810, 14 pages
Incomplete Time Series Prediction Using Max-Margin Classification of Data with Absent Features
1College of Computer Science, University of Chongqing, Chongqing 400030, China
2School of Mechatronic Engineering, Northwestern Polytechnical University, Xi'an 710072, China
Received 18 February 2010; Revised 24 March 2010; Accepted 20 April 2010
Academic Editor: Ming Li
Copyright © 2010 Shang Zhaowei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- R. H. Shumway and D. S. Stoffer, Time Series Analysis and Its Applications, Springer Texts in Statistics, Springer-Verlag, New York, Ny, USA, 2000.
- M. Li and J.-Y. Li, “On the predictability of long-range dependent series,” Mathematical Problems in Engineering, vol. 2010, Article ID 397454, 9 pages, 2010.
- E. G. Bakhoum and C. Toma, “Dynamical aspects of macroscopic and quantum transitions due to coherence function and time series events,” Mathematical Problems in Engineering, vol. 2010, Article ID 428903, 13 pages, 2010.
- M. Li and W. Zhao, “Variance bound of ACF estimation of one block of fGn with LRD,” Mathematical Problems in Engineering, vol. 2010, Article ID 560429, 14 pages, 2010.
- G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis: Forecasting and Control, Prentice Hall, Englewood Cliffs, NJ, USA, 3rd edition, 1994.
- M. Li, “Fractal time series-—a tutorial review,” Mathematical Problems in Engineering, vol. 2010, Article ID 157264, 26 pages, 2010.
- V. R. Vemuri and R. D. Rogers, Artificial Neural Networks: Forecasting Time Series, IEEE Computer Society Press, Los Alamitos, Calif, USA, 1993.
- L. J. Cao and F. E. H. Tay, “Support vector machine with adaptive parameters in financial time series forecasting,” IEEE Transactions on Neural Networks, vol. 14, no. 6, pp. 1506–1518, 2003.
- Y. Ning, L. Zuopeng, D. Yisheng, and W. Huoli, “SVM nonlinear regression algorithm,” Computer Engineering, vol. 31, no. 10, pp. 19–21, 2005.
- A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” Journal of the Royal Statistical Society. Series B, vol. 39, no. 1, pp. 1–38, 1977.
- Z. Ghahramani and M. I. Jordan, “Supervised learning from incomplete data via an EM approach,” in Advances in Neural Information Processing Systems (NIPS 6), pp. 120–127, Morgan Kauffman, San Fransisco, Calif, USA, 1994.
- D. B. Rubin, “Multiple Imputation after 18+ years,” Journal of the American Statistical Association, vol. 91, no. 434, pp. 473–489, 1996.
- I. Wasito and B. Mirkin, “Nearest neighbour approach in the least-squares data imputation algorithms,” Information Sciences, vol. 169, no. 1-2, pp. 1–25, 2005.
- S. Chiewchanwattana, C. Lursinsap, and C.-H. H. Chu, “Imputing incomplete time-series data based on varied-window similarity measure of data sequences,” Pattern Recognition Letters, vol. 28, no. 9, pp. 1091–1103, 2007.
- S. Prasomphan, C. Lursinsap, and S. Chiewchanwattana, “Imputing time series data by regional-gradient-guided bootstrapping algorithm,” in Proceedings of the 9th International Symposium on Communications and Information Technology (ISCIT '09), pp. 163–168, Incheon, South Korea, September 2009.
- G. Chechik, G. Heitz, G. Elidan, P. Abbeel, and D. Koller, “Max-margin classification of data with absent features,” Journal of Machine Learning Research, vol. 9, pp. 1–21, 2008.
- V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995.
- B. Schölkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization Optimization and Beyond, MIT Press, Cambridge, Mass, USA, 2002.
- W.-S. Chen, B. Pan, B. Fang, M. Li, and J. Tang, “Incremental nonnegative matrix factorization for face recognition,” Mathematical Problems in Engineering, vol. 2008, Article ID 410674, 17 pages, 2008.
- L. Q. Qi and J. Sun, “A nonsmooth version of Newton's method,” Mathematical Programming, vol. 58, no. 1–3, pp. 353–367, 1993.
- S. Y. Chen, Y. F. Li, and J. Zhang, “Vision processing for realtime 3-D data acquisition based on coded structured light,” IEEE Transactions on Image Processing, vol. 17, no. 2, pp. 167–176, 2008.
- J. A. C. Bingham, “Multicarrier modulation for data transmission: an idea whose time has come,” IEEE Communications Magazine, vol. 28, no. 5, pp. 5–14, 1990.
- M. Li and W. Zhao, “Representation of a stochastic traffic bound,” IEEE Transactions on Parallel and Distributed Systems. In press.
- G. Mattioli, M. Scalia, and C. Cattani, “Analysis of large amplitude pulses in short time intervals: application to neuron interactions,” Mathematical Problems in Engineering. In press.