GSTAR-X-SUR Model with Neural Network Approach on Residuals

Diana Rosyida, Atiek Iiriany, Nurjannah Nurjannah


One of the models that combine time and inter-location elements is Generalized Space Time Autoregressive (GSTAR) model. GSTAR model involving exogenous variables is GSTARX model. The exogenous variables which are used in GSTAR model can be both metrical and non-metrical data. Exogenous variable that can be applied into the forecasting of precipitation is non-metrical data which is in a form of precipitation intensity of a certain location. Currently, precipitation possesses patterns and characteristics difficult to identify, and thus can be interpreted as non-linear phenomenon. Non-linear model which is much developed now is neural network. Parameter estimation method employed is Seemingly Unrelated Regression (SUR) model approach, which can solve the correlation between residual models. This current research employed GSTARX-SUR modelling with neural network approach on residuals. The data used in this research were the records of 10-day precipitations in four regions in West Java, namely Cisondari, Lembang, Cianjur, and Gunung Mas, from 2005 to 2015. The GSTARX-SUR NN modelling resulted in precipitation deviation average of the forecast and the actual data at 4.1385 mm. This means that this model can be used as an alternative in forecasting precipitation.


GSTAR-X, SUR, neural network, precipitation

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