Set pair analysis based on phase space reconstruction model and its application in forecasting extreme temperature

Mathematical Problems in Engineering

Volumn 2013, Issue , 2013, Pages

  Zhang, Yin ^a   Yang, Xiao Hua ^a   Zhang, Ling ^b   Ma, Wan Ying ^a   Qiao, Ling Xia ^a  

^a BEIJING NORMAL UNIVERSITY   (China)

^b AUBURN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACK-PROPAGATION NEURAL NETWORKS; EMBEDDING DIMENSIONS; EXTREME TEMPERATURES; ITS APPLICATIONS; MEAN RELATIVE ERROR; NUMERICAL FORECASTING; PHASE SPACE RECONSTRUCTION; SET PAIR ANALYSIS;

PHASE SPACE METHODS; REGRESSION ANALYSIS;

FORECASTING;

EID: 84884234794     PISSN: 1024123X     EISSN: 15635147     Source Type: Journal    
DOI: 10.1155/2013/516150     Document Type: Article

References (33)

1. Sahoo G. B., Schladow S. G., Impacts of climate change on lakes and reservoirs dynamics and restoration policies. Sustainability Science 2008 3 2 189 199 2-s2.0-56049084197 10.1007/s11625-008-0056-y
2. Bošnjaković B., Geopolitics of climate change: a review. Thermal Science 2012 16 3 629 654
3. Matic B., Tepić J., Sremac S., Development and evaluation of the model for the surface pavement temperature prediction. Metalurgija 2012 51 3 329 332
4. Hippert H. S., Pedreira C. E., Souza R. C., Combining neural networks and ARIMA models for hourly temperature forecast. 4 Proceedings of the International Joint Conference on Neural Networks (IJCNN '00) July 2000 414 419 2-s2.0-0033721974
5. Liu F. G., Zuo Y., The application of rank set pair analysis similar prediction model in futures price forecasting. Proceedings of the International Conference 2009 Beijing, China 240 243
6. Li W., Qiu L., Chen X., Huang Q., Assessment model for river ecology health based on set pair analysis and variable fuzzy set. Journal of Hydraulic Engineering 2011 42 7 775 782 2-s2.0-80052076950
7. Holden K., Vector auto regression modeling and forecasting. Journal of Forecasting 1995 14 3 159 166
8. Bańbura M., Giannone D., Reichlin L., Large Bayesian vector auto regressions. Journal of Applied Econometrics 2010 25 1 71 92 10.1002/jae.1137 MR2751790
9. Yang S. S., Hua X. H., Jiang R., Zhang Y. C., New optimal weight combination model for forecasting precipitation. Mathematical Problems in Engineering 2012 2012 13 376010 10.1155/2012/376010
10. Yang S., Neural network forecast under the organic hybrid model of genetic algorithm and particle swarm algorithm. 1, 2 Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR '08) August 2008 254 258 2-s2.0-56749184546 10.1109/ICWAPR.2008.4635785
11. Mei Y., Yang X. H., Chang C. C., Set pair analysis for optimal selection of bioretention media. Advanced Science Letters 2012 10 1 687 689
12. Guo T. N., Yang X. H., Mei Y., Set pair analysis of eco-environment quality using maximum entropy. Advanced Science Letters 2012 10 1 640 643
13. Cao L., Hong Y., Fang H., He G., Predicting chaotic time series with wavelet networks. Physica D 1995 85 1-2 225 238 2-s2.0-58149210009
14. Farmer J. D., Sidorowich J. J., Predicting chaotic time series. Physical Review Letters 1987 59 8 845 848 10.1103/PhysRevLett.59.845 MR902527
15. Jeong D. I., Kim Y., Combining single-value streamflow forecasts-a review and guidelines for selecting techniques. Journal of Hydrology 2009 377 3-4 284 299 2-s2.0-70349765658 10.1016/j.jhydrol.2009.08.028
16. Khatibi R., Sivakumar B., Ghorbani M. A., Kisi O., Koçak K., Farsadi Zadeh D., Investigating chaos in river stage and discharge time series. Journal of Hydrology 2012 414-415 108 117 2-s2.0-84855192809 10.1016/j.jhydrol.2011.10.026
17. Di C. L., Yang X. H., Huang D. W., Chaos control and synchronization of a nonlinear system. Nonlinear Science Letters C 2012 2 2 47 54
18. Liu Z., Chaotic time series analysis. Mathematical Problems in Engineering 2010 2010 31 720190 MR2610516 ZBL1191.37046 10.1155/2010/720190
19. Yang X. H., She D. X., Yang Z. F., Tang Q. H., Li J. Q., Chaotic bayesian method based on multiple criteria decision making (MCDM) for forecasting nonlinear hydrological time series. International Journal of Nonlinear Sciences and Numerical Simulation 2009 10 11-12 1595 1610 2-s2.0-77950411521
20. She D., Yang X., A new adaptive local linear prediction method and its application in hydrological time series. Mathematical Problems in Engineering 2010 2010 15 2-s2.0-77955288856 10.1155/2010/205438 205438
21. Takens F., Rand D. A., Young L. S., Detecting strange attractors in turbulence. Dynamical Systems and Turbulence 1981 898 Berlin, Germany Springer 366 381 Lecture Notes in Mathematics MR654900 10.1007/BFb0091924 ZBL0513.58032
22. Rosenstein M. T., Collins J. J., De Luca C. J., A practical method for calculating largest Lyapunov exponents from small data sets. Physica D 1993 65 1-2 117 134 10.1016/0167-2789(93)90009-P MR1221250 ZBL0779.58030
23. Fraser A. M., Swinney H. L., Independent coordinates for strange attractors from mutual information. Physical Review A 1985 33 2 1134 1140 10.1103/PhysRevA.33.1134 MR823473 ZBL1184.37027
24. Rosenstein M. T., Collins J. J., De Luca C. J., Reconstruction expansion as a geometry-based framework for choosing proper delay times. Physica D 1994 73 1 82 98 10.1016/0167-2789(94)90226-7 MR1272841
25. Li M., Zhao W., Smoothing the sample autocorrelation of long-range dependent traffic. Mathematical Problems in Engineering 2013 2013 10 631498 10.1155/2013/631498
26. Li M., Zhao W., Quantitatively investigating locally weak stationarity of modified multifractional Gaussian noise. Physica A 2012 391 24 6268 6278
27. Lai Y., Lerner D., Effective scaling regime for computing the correlation dimension from chaotic time series. Physica D 1998 115 1-2 1 18 2-s2.0-0002587376
28. Grassberger P., Procaccia I., Measuring the strangeness of strange attractors. Physica D 1983 9 1 189 208 10.1016/0167-2789(83)90298-1 MR732572 ZBL0593.58024
29. Kennel M. B., Brown R., Abarbanel H. D. I., Determining embedding dimension for phase-space reconstruction using a geometrical construction. Physical Review A 1992 45 6 3403 3411 2-s2.0-35949006791 10.1103/PhysRevA.45. 3403
30. Abarbanel H. D. I., Brown R., Sidorowich J. J., Tsimring L. S., The analysis of observed chaotic data in physical systems. Reviews of Modern Physics 1993 65 4 1331 1392 2-s2.0-12044249591 MR1249699 10.1103/RevModPhys.65.1331
31. Cao L., Practical method for determining the minimum embedding dimension of a scalar time series. Physica D 1997 110 1 43 50 2-s2.0-0001874436
32. Price J. C., Land surface temperature measurements from the split window channels of the NOAA 7 advanced very high resolution radiometer. Journal of Geophysical Research D 1984 89 5 7231 7237 2-s2.0-0021644369
33. Easterling D. R., Meehl G. A., Parmesan C., Changnon S. A., Karl T. R., Mearns L. O., Climate extremes: observations, modeling, and impacts. Science 2000 289 5487 2068 2074 2-s2.0-0034703308 10.1126/science.289.5487.2068