A new adaptive local linear prediction method and its application in hydrological time series

Mathematical Problems in Engineering

Volumn 2010, Issue , 2010, Pages

  Yang, Xiaohua ^a   She, Dunxian ^a  

^a BEIJING NORMAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CHAOTIC TIME SERIES; EMBEDDING DIMENSIONS; GENERALIZED DEGREE OF FREEDOM; HYDROLOGICAL TIME-SERIES; LOCAL LINEAR; NEAREST NEIGHBORS; OPTIMAL PARAMETER; PARAMETER SET; PREDICTION PROCESS; TWO PARAMETER;

OPTIMIZATION; TIME SERIES;

FORECASTING;

EID: 77955288856     PISSN: 1024123X     EISSN: 15635147     Source Type: Journal    
DOI: 10.1155/2010/205438     Document Type: Article

References (35)

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. Lin C. A., Wen L., Lu G., Wu Z., Zhang J., Yang Y., Zhu Y., Tong L., Real-time forecast of the 2005 and 2007 summer severe floods in the Huaihe River Basin of China Journal of Hydrology 2010 381 1-2 33 41
3. Cattani C., Kudreyko A., On the discrete harmonic wavelet transform Mathematical Problems in Engineering 2008 2008 7
4. Cattani C., Harmonic wavelet approximation of random, fractal and high frequency signals Telecommunication Systems 2010 43 3-4 207 217
5. Li M., Zhao W., Representation of a stochastic traffic bound. IEEE Transactions on Parallel and Distributed Systems. In press
6. Li M., Modeling autocorrelation functions of long-range dependent teletraffic series based on optimal approximation in Hilbert spacea further study Applied Mathematical Modelling 2007 31 3 625 631
7. Li M., Lim S. C., Modeling network traffic using generalized Cauchy process Physica A 2008 387 11 2584 2594
8. Li M., Zhao W., Variance bound of ACF estimation of one block of fGn with LRD Mathematical Problems in Engineering 2010 2010 14
9. Shang P., Na X., Kamae S., Chaotic analysis of time series in the sediment transport phenomenon Chaos, Solitons and Fractals 2009 41 1 368 379
10. Lorenz E. N., Deterministic nonperiodic flow Journal of the Atmospheric Sciences 1963 20 130 141
11. May R. M., Simple mathematical models with very complicated dynamics Nature 1976 261 5560 459 467
12. Hnon M., A two-dimensional mapping with a strange attractor Communications in Mathematical Physics 1976 50 1 69 77
13. Mackey M. C., Glass L., Oscillation and chaos in physiological control systems Science 1977 197 4300 287 289
14. Lorenz E. N., Atmospheric predictability as revealed by naturally occurring analogues Journal of the Atmospheric Sciences 1969 26 4 636 646
15. 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
16. Grassberger P., Procaccia I., Characterization of strange attractors Physical Review Letters 1983 50 5 346 349
17. Sahoo G. B., Schladow S. G., Reuter J. E., Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models Journal of Hydrology 2009 378 3-4 325 342
18. Hense A., On the possible existence of a strange attractor for the southern oscillation Beitraege zur Physik der Atmosphaere 1987 60 1 34 47
19. Cao L., Hong Y., Fang H., He G., Predicting chaotic time series with wavelet networks Physica D 1995 85 1-2 225 238
20. Farmer J. D., Sidorowich J. J., Predicting chaotic time series Physical Review Letters 1987 59 8 845 848
21. Jeong D. I., Kim Y.-O., Combining single-value streamflow forecastsa review and guidelines for selecting techniques Journal of Hydrology 2009 377 3-4 284 299
22. Abarbanel H. D. I., Analysis of Observed Chaotic Data 1996 2nd New York, NY, USA Springer xiv+272 Institute for Nonlinear Science
23. 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
24. Aleksi Z., Estimating the embedding dimension Physica D 1991 52 2-3 362 368
25. Cao L., Practical method for determining the minimum embedding dimension of a scalar time series Physica D 1997 110 1-2 43 50
26. Broomhead D. S., King G. P., Extracting qualitative dynamics from experimental data Physica D 1986 20 2-3 217 236
27. Vautard R., Yiou P., Ghil M., Singular-spectrum analysis: a toolkit for short, noisy chaotic signals Physica D 1992 58 14 95 126
28. Mees A. I., Rapp P. E., Jennings L. S., Singular-value decomposition and embedding dimension Physical Review A 1987 36 1 340 346
29. Palu M., Dvok I., Singular-value decomposition in attractor reconstruction: pitfalls and precautions Physica D 1992 55 1-2 221 234
30. Fraser A. M., Reconstructing attractors from scalar time series: a comparison of singular system and redundancy criteria Physica D 1989 34 3 391 404
31. Meng Q.-F., Peng Y.-H., Xue P.-J., A new method of determining the optimal embedding dimension based on nonlinear prediction Chinese Physics 2007 16 5 1252 1257
32. Jayawardena A. W., Li W. K., Xu P., Neighbourhood selection for local modelling and prediction of hydrological time series Journal of Hydrology 2002 258 14 40 57
33. 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
34. Sivakumar B., Jayawardena A. W., Fernando T. M., River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches Journal of Hydrology 2002 265 14 225 245
35. Ye J., On measuring and correcting the effects of data mining and model selection Journal of the American Statistical Association 1998 93 441 120 131