A non-homogeneous hidden Markov model for predicting the distribution of sea surface elevation

Journal of Applied Statistics

Volumn 41, Issue 2, 2014, Pages 294-319

  Hokimoto, Tsukasa ^a   Shimizu, Kunio ^b  

^a UNIVERSITY OF TOKYO   (Japan)

^b KEIO UNIVERSITY   (Japan)

Author keywords

circular hidden Markov model; non homogeneous hidden Markov model; prediction; sea state; wind direction; wind speed

Indexed keywords

EID: 84889087996     PISSN: 02664763     EISSN: 13600532     Source Type: Journal    
DOI: 10.1080/02664763.2013.839634     Document Type: Article

References (24)

1. A.V. Babanin and V.G. Polnikov, Non Gaussian pattern of wind waves, Morsk, Gidrofiz. Zh. 3 (1994), pp. 79-82.
2. L.E. Baum and T. Petrie, Statistical inference for probabilistic functions of finite state Markov chains, Ann. Math. Stat. 37 (1966), pp. 1554-1563. doi: 10.1214/aoms/1177699147
3. G.E.P. Box and G.M. Jenkins, Time Series Analysis, Forecasting and Control, 2nd ed., Holden-Day, San Francisco, CA, 1976.
4. P.J. Brockwell and R.A. Davis, Introduction to Time Series and Forecasting, Springer, New York, 1996.
5. L. De Angelis and L.J. Paas, A dynamic analysis of stock markets using a hidden Markov model, J. Appl. Stat. 40(8) (2013), pp. 1682-1700. doi: 10.1080/02664763.2013.793302
6. E. Erdem and J. Shi, ARMA based approaches for forecasting the tuple of wind speed and direction, Appl. Energy, 88 (2011), pp. 1405-1414. doi: 10.1016/j.apenergy.2010.10.031
7. T. Hokimoto, Prediction of wave height based on the monitoring of surface wind, in Oceanography, M. Marcelli, ed., InTech, Rijeka, Croatia, 2012, pp. 169-188.
8. T. Hokimoto and K. Shimizu, An angular-linear times series model for wave height prediction, Ann. Inst. Stat. Math. 60 (2008), pp. 781-800. doi: 10.1007/s10463-008-0207-z
9. H. Holzmann, A. Munk, M. Suster, and W. Zucchini, Hidden Markov models for circular and linear-circular time series, Environ. Ecol. Stat. 13 (2006), pp. 325-347. doi: 10.1007/s10651-006-0015-7
10. N.E. Huang, S.R. Long, C.C. Tung, Y. Yuan, and L.F. Bliven, A non-Gaussian statistical model for surface elevation of nonlinear random wave fields, J. Geophys. Res. 88 (1983), pp. 7597-7606. doi: 10.1029/JC088iC12p07597
11. J.P. Hughes, A class of stochastic models for relating synoptic atmospheric patterns to local hydrologic phenomena, Ph.D. diss., University of Washington, 1993.
12. J.P. Hughes and P. Guttorp, A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena, Water Resources Research 30 (1994), pp. 1535-1546. doi: 10.1029/93WR02983
13. B. Kinsman, Wind Waves; Their Generation and Propagation on the Ocean Surface, Prentice-Hall, Engle wood Cliffs, NJ, 1965.
14. F. Lagona, A. Maruotti, and M. Picone, A non-homogeneous hidden Markov model for the analysis of multi-pollutant exceedances data, in Hidden Markov Models, Theory and Applications, P. Dymarsky, ed., InTech, Rijeka, Croatia, 2011, pp. 207-222.
15. H. Liu, E. Erdem, and J. Shi, Comprehensive evaluation of ARMA-GARCH(-M) approaches for modeling the mean and volatility of wind speed, Appl. Energy. 88 (2011), pp. 724-732. doi: 10.1016/j.apenergy.2010.09.028
16. M.S. Longuet-Higgins, The effect of nonlinearities on statistical distributions in the theory of sea waves, J. Fluid Mech. 17(3) (1963), pp. 459-480. doi: 10.1017/S0022112063001452
17. L.J. Paas, J.K. Vermunt, and T.H.A. Bijmolt, Discrete time, discrete class latent Markov modeling for assessing and predicting household acquisitions of financial products, J. R. Stat. Soc. Ser. A. 170(4) (2007), pp. 955-974. doi: 10.1111/j.1467-985X.2007.00478.x
18. K. Philippopoulos and D. Deligiorgi, Statistical simulation of wind speed in Athens, Greece, based on Weibull and ARMA models, Int. J. Energy Environ. 4 (2009), pp. 151-158.
19. A.W. Robertson, S. Kirshner, and P. Smyth, Downscaling of daily rainfall occurrence over Northeast Brazil using a hidden Markov model, J. Climate 17 (2004), pp. 4407-4424. doi: 10.1175/JCLI-3216.1
20. M.A. Tayfun, On narrow-band representation of ocean waves, I. Theory, J. Geophys. Res. 91 (1986), pp. 7743-7759. doi: 10.1029/JC091iC06p07743
21. R.S.J. Tol, Autoregressive conditional heteroscedasticity in daily wind speed measurements, Theoret. Appl. Climatol. 56 (1997), pp. 113-122. doi: 10.1007/BF00863788
22. A.S. Zapevalov, A.N. Bol'shakov, and V.E. Smolov, Simulating of the probability density of sea surface elevations using the Gram-Charlier series, Oceanology 51 (2011), pp. 407-414. doi: 10.1134/S0001437011030222
23. W. Zucchini and P. Guttorp, A hidden Markov model for space-time precipitation, Water Resour. Res. 27 (1991), pp. 1917-1923. doi: 10.1029/91WR01403
24. W. Zucchini and I.L. MacDonald, Hidden Markov models for Time Series, An Introduction Using R, Chapman & Hall, Boca Raton, FL, 2009.