G-STAR Model for Forecasting Space-Time Variation of Temperature in Northern Ethiopia

Year 2018, , 9 - 19, 01.09.2018

Mulugeta Aklilu Zewdie , Gebretsadik G Wubit Amare W Ayele

https://doi.org/10.34110/forecasting.437599

Cited By: 2

Abstract

Among many indicators of climate change, the temperature is a key indicator to take remedial action for world global warming. This finding provides application of space-time models for temperature data, which is selected in three meteorology stations (Mekelle, Adigrat and Adwa) of Northern Ethiopia. The objectives of this research are to see the space-time variations of temperature and to find better forecasting model. The steps for building this model starting from order selection of space and autoregressive order, parameters estimation, a diagnostic check of errors and finally forecasting for the long term. The preliminary model is identified by VAR (vector autoregressive) model and tentatively selects the order by using MIC (minimum information criteria) and uses the autoregressive order for the model and fixes the spatial effect, model parameters are estimated using the least square method. Weighted matrix computed by using queen contiguity criteria. It is found that the model STAR(1,1) and GSTAR(1,1) are two options, finally the best-fitted model is GSTAR(1,1) which has high forecasting performance and smallest RMSEF. The outcome of the forecast indicated that in northern Ethiopia, the weather conditions especially temperature of future is increasing trend in dry seasons in all 3 stations in similar fashion but more consistent and has less variation across the region, and less consistent and high variation within the region and the researcher found that spatial effect has high impact on prediction of models.

Keywords

Temperature, STAR/GSTAR, Forecasting

References

• Avit Kumar Bhowmik and Pedro Cabral Space-Time Variability of Summer Temperature Field over Bangladesh during 1948-2007 ICCSA 2013, Part IV, LNCS 7974, pp. 120–135, 2013.Springer-Verlag Berlin Heidelberg
• Barnett and O’Neill(2001), Maladaptation, Global Environmental Change, IPCC, pp 211–213
• Borovkova, H. P. Lopuhaa, and B. N. Ruchjana,( 2008) Consistency of asymptotic normality of least squares estimators in generalized STAR models, Statistica Neerlandica, vol. 62, no. 4, pp. 482–508.
• Brockwell, Peter J. 2002 Introduction to time series and forecasting 2nd ed. Springer texts in statistics.
• D.E.parker and E.B. Horton, (1999) Global and Regional Climate in 1998, Weather, vol 54 pp 173-184
• Dhoriva Urwatul Wutsqa, Suhartono and Brodjol Sutijo: Generalized Space-Time Autoregressive Modeling Proceedings of the 6th IMT-GT Conference on Mathematics, Statistics and its Applications (ICMSA2010) Universiti Tunku Abdul Rahman, Kuala Lumpur, Malaysia 753
• DOUGLAS C. MONTGOMERY 2008 Introduction to Time Series Analysis and Forecasting A JOHN WILEY &. SONS, INC., PUBLICATION
• Intergovernmental Panel on Climate Change (IPCC 2001): Synthesis report, Contribution of Working groups I and III to the Third assessment of the IPCC,” Cambridge university press. Cambridge
• Jonathan D. Cryer • Kung-Sik Chan 2008 Time Series Analysis With Applications in R Second Edition Springer Science+Business Media, LLC
• K.Y.Vinnikov and N.C.Grodly,(2003)“ Global Warming Trends of Mean Tropospheric Temperature by Satellites,” Science Vol.302, pp. 269-272
• L. Martin and J. E. Oeppen, (1975) “The identification of regional forecasting,” Transactions of the Institute of British Geographers,” vol. 66, pp. 119–128
• NICOLAS R. DALEZIOS & KAZIMIERZ ADAMOWSKI (1995) Spatio-temporal precipitation modelling in rural watersheds, Hydrological Sciences Journal, 40:5, 553-568, DOI: 10.1080/02626669509491444
• Nurhayati, Nunung.(2012) Application of generalized space-time autoregressive model on GDP data in west European countries. Journal of Probability and Statistics, Hindawi Publishing Corporation Volume 2012, PP. 1-16
• P.D.Jones, T.M.L. Wigley and P.B. Wright,(1986 )“ Global Temperature Variations between 1861 and 1984, “ Nature, Vol.322, pp. 430-434.
• Pfeifer and S. J. Deutsch, (1980)“A three-stage iterative procedure for space-time modeling,” Technometrics, vol. 22, pp. 397–408.
• R.A.pielke , (2005 )“ Land use and climate change,” Science , vol.310, pp.54-57
• Richard Harris and Robert Sollis 2003 Applied Time Series Modelling and Forecasting John Wiley & Sons Ltd, The Atrium, Southern Gate. Chichester. West Sussex PO19 8SQ, England
• Robert H. Shumway David S. Stoffer Time Series Analysis and Its Applications With R Examples Third edition. Springer New York Dordrecht Heidelberg London
• Terzi, ( 1995) “Maximum likelihood estimation of a GSTAR1;1 model,” Statistical Methods and Applications, vol. 3, pp. 377–393.
• Utriweni Mukhaiyar & Udjianna S. Pasaribu A New Procedure for Generalized STAR Modeling using IAcM Approach: ITB J. Sci., Vol. 44 A, No. 2, 2012, 179-192 179
• V. P. R. Silva,(2004)“On Climate Variability in North-East of Brazil,” Journal of Arid Environments, Vol. 58, No. 4, pp. 575-596.
• Wei, William W.S 2006 Time series analysis: Univariate and multivariate methods second edition: Pearson education inc.
• XU-FENG NIU, IAN W. MCKEAGUE and JAMES B. ELSNER Seasonal Space-Time Models for Climate Systems Statistical Inference for Stochastic Processes 6: 111–133, Kluwer Academic Publishers. Netherlands.