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A SEASONAL AUTO-REGRESSIVE MODEL BASED SUPPORT VECTOR REGRESSION PREDICTION METHOD FOR H5N1 AVIAN INFLUENZA ANIMAL EVENTS

Decision Systems & e-Service Intelligence Lab, Centre for Quantum Computation & Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, P.O. Box 123, Broadway, NSW 2007, Australia

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JIE LU

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GUANGQUAN ZHANG

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https://doi.org/10.1142/S1469026811003069Cited by:3 (Source: Crossref)

Abstract

The time series prediction of avian influenza epidemics is a complex issue, because avian influenza has latent seasonality which is difficult to identify. Although researchers have applied a neural network (NN) model and the Box-Jenkins model for the seasonal epidemic series research area, the results are limited. In this study, we develop a new prediction seasonal auto-regressive-based support vector regression (SAR-SVR) model which combines the seasonal auto-regressive (SAR) model with a support vector regression (SVR) model to address this prediction problem to overcome existing limitations. Fast Fourier transformation is also merged into this method to identify the latent seasonality inside the time series. The experiments demonstrate that the developed SAR-SVR method out-performs SVR, Box-Jenkins models and two layer feed forward NN model-both in accuracy and stability in the avian influenza epidemic disease time series prediction.

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References

J. Liuet al., Science 309, 1206 (2005), DOI: 10.1126/science.1115273. Crossref, Google Scholar
H. Chenet al., Nature 436, 191 (2005), DOI: 10.1038/nature03974. Crossref, Google Scholar
M. J. Keeling and K. T. D. Eames, Journal of the Royal Society Interface 2, 295 (2005), DOI: 10.1098/rsif.2005.0051. Crossref, Google Scholar
C. P. Jewellet al., Preventive Veterinary Medicine 91, 19 (2009), DOI: 10.1016/j.prevetmed.2009.05.019. Crossref, Google Scholar
T. Tiensinet al., The Journal of Infectious Diseases 196, 1679 (2007), DOI: 10.1086/522007. Crossref, Google Scholar
M. Wardet al., Epidemiology and Infection 137, 219 (2009). Crossref, Google Scholar
A. Boumaet al., PLoS Pathog 5, (2009), DOI: 10.1371/journal.ppat.1000281. Google Scholar
H. Trottier, P. Philippe and R. Roy, Emerg. Themes Epidemiol. 3, 9 (2006), DOI: 10.1186/1742-7622-3-9. Crossref, Google Scholar
P. Zucset al., Emerging Themes in Epidemiology 2, 6 (2005), DOI: 10.1186/1742-7622-2-6. Crossref, Google Scholar
T. A. Abekuet al., Trop. Med. Int. Health 7, 851 (2002), DOI: 10.1046/j.1365-3156.2002.00924.x. Crossref, Google Scholar
D. C. Medinaet al., PLoS ONE 2, e1181 (2007), DOI: 10.1371/journal.pone.0001181. Crossref, Google Scholar
G. E. P. Box and G. Jenkins , Time Series Analysis: Forecasting and Control ( Holden-Day , San Francisco , 1970 ) . Google Scholar
B. L. Smith, B. M. Williams and R. Keith Oswald, Transportation Research Part C: Emerging Technologies 10, 303 (2002), DOI: 10.1016/S0968-090X(02)00009-8. Crossref, Google Scholar
R. D. Lee and L. R. Carter, Journal of the American Statistical Association 87, 659 (1992), DOI: 10.2307/2290201. Google Scholar
A. Harvey, The Economic Journal 107, 192 (1997), DOI: 10.1111/1468-0297.00152. Crossref, Google Scholar
C. Chatfield and D. L. Prothero, Journal of the Royal Statistical Society, Series A (General) 136, 295 (1973), DOI: 10.2307/2344994. Crossref, Google Scholar
S. Raza Abidi and A. Goh, Applying knowledge discovery to predict infectious disease epidemics, PRICAI'98: Topics in Artificial Intelligence (1998) pp. 170–181. Google Scholar
L. Xiao-Yi , X. Zhao-Di and L. Zhen-Peng , Wavelet neural network predication for epidemic outbreak , Proceedings of the 21st Annual International Conference on Chinese Control and Decision Conference ( IEEE Press Guilin , China , 2009 ) . Google Scholar
C. Vairappanet al., International Journal of Computational Intelligence and Applications 8, 429 (2009). Link, Google Scholar
S. H. Linget al., International Journal of Computational Intelligence and Applications 5, 371 (2005). Link, Google Scholar
S. M. F. M. H. M. S. A. R. Moghaddamnia, Journal of Applied Sciences 9, 10 (2009). Google Scholar
I. R. Ruiz and H. Pomares, Soft-computing techniques for time series forecasting, ESANN'04 (2004) pp. 93–102. Google Scholar
R. Samsudin, A. Shabri and P. Saad, J. Applied Sci. 10, 950 (2010). Crossref, Google Scholar
A. Hossainet al., Comparison of GARCH, Neural Network and Support Vector Machine in Financial Time Series Prediction, Pattern Recognition and Machine Intelligence (2009) pp. 597–602. Google Scholar
M. Brameret al., Artificial Intelligence in Theory and Practice (Springer, Boston, 2006) pp. 149–158. Crossref, Google Scholar
W. Chun-Hsin, H. Jan-Ming and D. T. Lee, Intelligent Transportation Systems, IEEE Transactions 5, 276 (2004). Google Scholar
K.-J. Kim, Neurocomputing 55, 307 (2003), DOI: 10.1016/S0925-2312(03)00372-2. Crossref, Google Scholar
F. E. H. Tay and L. Cao, Omega 29, 309 (2001), DOI: 10.1016/S0305-0483(01)00026-3. Crossref, Google Scholar
W.-C. Hong, Applied Mathematical Modelling 33, 2444 (2009). Crossref, Google Scholar
Z. Yuan, BMC Bioinformatics 6, 248 (2005), DOI: 10.1186/1471-2105-6-248. Crossref, Google Scholar
J. Wu, M. Liu and L. Jin, International Journal of Computational Intelligence and Applications 9, 87 (2010). Link, Google Scholar
V. Fernandez, Resources Policy 32, 80 (2010). Crossref, Google Scholar
A. C. Harvey and N. Shephard, Handbook of Statistics (Elsevier, 1993) pp. 261–302. Google Scholar
B. Cazelleset al., Journal of the Royal Society Interface 4, 625 (2007), DOI: 10.1098/rsif.2007.0212. Crossref, Google Scholar
G. Constantin de Magnyet al., BMC Infectious Diseases 7, 20 (2007). Crossref, Google Scholar
J. A. K. Suykenset al., Neurocomputing 48, 85 (2002), DOI: 10.1016/S0925-2312(01)00644-0. Crossref, Google Scholar
V. N. Vapnik , Statistical Learning Theory ( Wiley , New York , 1998 ) . Google Scholar
S. Bernhardet al., Neural Comput. 12, 1207 (2000). Crossref, Google Scholar
G. E. P. Box and N. R. Draper , Empirical model-building and response surfaces , eds. George E. P. Box and Norman R. Draper ( John Wiley , New York Singapore , 1987 ) . Google Scholar
L. Stone, R. Olinky and A. Huppert, Nature 446, 533 (2007), DOI: 10.1038/nature05638. Crossref, Google Scholar
Y. L. Si, P. Debba, A. K. Skidmore, A. G. Toxopeus and L. Li, Spatial and temporal patterns of global H5N1 outbreaks, in (International Society for Photogrammetry and Remote Sensing, 2008) . Google Scholar
D. U. Pfeifferet al., The Veterinary Journal 174, 302 (2007). Crossref, Google Scholar
M. Gauthier-Clerc, C. Lebarbenchon and F. Thomas, Ibis 149, 202 (2007). Crossref, Google Scholar
C.-C. Chang and C.-J. Lin, LIBSVM: A Library for Support Vector Machines (2001). Google Scholar
V. Cherkassky and Y. Ma, Neural Networks 17, 113 (2004). Crossref, Google Scholar