Application of RBF-STARMA Model in Shipping Flow Forecasting

Hong Qiong Huang; Shu Lan Lin; Tian Hao Tang; Ji Fang Li

doi:10.4028/www.scientific.net/AMR.108-111.893

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 108-111Application of RBF-STARMA Model in Shipping Flow...

Application of RBF-STARMA Model in Shipping Flow Forecasting

Abstract:

Based on the idea of the neural network, intelligent computing methods are used to analyze temporal and spatial data. We present the temporal and spatial autocorrelation moving average (STARMA) model based on the in-depth systematic study on time sequence of hybrid model. Firstly this paper uses radial basis function neural network to extract the temporal and spatial sequence which is non-stationary caused by large-scale non-linear trend, secondly this paper presents STARMA modeling of small-scale random spatial and temporal variation. Comparative analysis between the original data and the forecasting data shows that proposed hybrid model has better performance of fitting and generalization.

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Periodical:

Advanced Materials Research (Volumes 108-111)

Pages:

893-897

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.108-111.893

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Online since:

May 2010

Authors:

Hong Qiong Huang, Shu Lan Lin, Tian Hao Tang, Ji Fang Li

Keywords:

Radial Basis Function (RBF), Shipping Flow Forecast, Space-Time Series, STARMA

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References

[1] Alvarez, A., 2003. Performance of satellite based ocean forecasting (SOFT) systems: a study in the Adriatic Sea. J. Atmos. Ocean. Technol. 20, 717-729.

DOI: 10.1175/1520-0426(2003)20<717:posbof>2.0.co;2

Google Scholar

[2] Alvarez, A., Lopez, C., Riera, M., Hernandez-Garcia, E., Tintore, J., 2000. Forecasting the SST space-time variability of the Alboran Sea with genetic algorithms. Geophys. Res. Lett. 27, 2709-2712.

DOI: 10.1029/1999gl011226

Google Scholar

[3] Alvarez, A., Orfila, A., Tintore, J., 2001. Darwin-an evolutionary program for nonlinear modeling of chaotic time series. Compute. Phys. Commun. 136, 334-349.

DOI: 10.1016/s0010-4655(01)00154-0

Google Scholar

[4] A. ClauBnitzer, P. Nevir. Analysis of quantitative precipitation forecasts using the Dynamic State Index. Preprint submitted to Atmospheric Research, 2009, 22.

Google Scholar

[5] R.J. Ong, J.T. Dawley and P.G. Clem: submitted to Journal of Materials Research (2003) N. Schanne, R. Wapler, A. Weyh. Regional unemployment forecasts with spatial interdependencies. International Journal of Forecasting, (2009).

DOI: 10.1016/j.ijforecast.2009.07.002

Google Scholar

[6] Pascual, A., Buongiorno Nardelli, B., Larnicol, G., Emelianov, M., Gomis, D., 2002. A case of an intense eddy in the Balearic Sea (western Mediterranean). J. Geophys. Res. 107, 3183-3190.

DOI: 10.1117/12.411708

Google Scholar

[7] Principal Component Analysis in Meteorology and Oceanography. Elsevier, New York.

Google Scholar