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Information Journal Paper

Title

THE PERSIAN GULF SURFACE TEMPERATURE FORECASTING USING THE VECTOR AUTOREGRESSIVE PROCESSES

Pages

  53-61

Abstract

 The MULTIVARIATE TIME SERIES models as the linear stochastic models were used to forecast monthly average of the PERSIAN GULF surface temperature (PGST). The time series data of temperature for the period 1854-2007 for six sea grid points were considered as the input file of MULTIVARIATE TIME SERIES. VECTOR AUTOREGRESSIVE PROCESSES were employed to perform MULTIVARIATE TIME SERIES. The auto-correlation function plot of the residuals for the fitted model, based on the Akaike's information criterion, has indicated that the residuals were uncorrelated. The monthly values of the PGST from January 2008 to December 2009 as the test data have been forecasted using the fitted models. The results have indicated that the correlation coefficient between the observed and predicted values for each grid point was about 0.99. The root mean square prediction error for each grid point was less than 0.7 degrees Celsius.

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    • Cite

    APA: Copy

    JAHANDIDEH, M., & SHIRVANI, A.. (2011). THE PERSIAN GULF SURFACE TEMPERATURE FORECASTING USING THE VECTOR AUTOREGRESSIVE PROCESSES. WATER ENGINEERING, 4(8), 53-61. SID. https://sid.ir/paper/169640/en

    Vancouver: Copy

    JAHANDIDEH M., SHIRVANI A.. THE PERSIAN GULF SURFACE TEMPERATURE FORECASTING USING THE VECTOR AUTOREGRESSIVE PROCESSES. WATER ENGINEERING[Internet]. 2011;4(8):53-61. Available from: https://sid.ir/paper/169640/en

    IEEE: Copy

    M. JAHANDIDEH, and A. SHIRVANI, “THE PERSIAN GULF SURFACE TEMPERATURE FORECASTING USING THE VECTOR AUTOREGRESSIVE PROCESSES,” WATER ENGINEERING, vol. 4, no. 8, pp. 53–61, 2011, [Online]. Available: https://sid.ir/paper/169640/en

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