Forecasting the amount of rainfall in West Kalimantan using Generalized Space-time Autoregressive model. Issue 1 (April 2021) - Utami, R; Nurhayati, N; Maryani, S
Abstract: The generalized space-time autoregressive or GSTAR is a space-time model which can be used to analyze time-series data in several locations considered to be correlated. In this research, the GSTAR model is applied to forecast the amount of rainfall in West Kalimantan, especially at Sintang Station, Melawi Station, and Ketapang Station. The data used for modeling is data of the amount of rainfall for the period January 2013-December 2017, while the data used for model validation is data for the period January 2018-December 2018. The spatial weights used are uniform weights, inverse distance weights, and normalized cross-correlation weights, and the estimation method used is the ordinary least square or OLS estimation. The best model is selected based on the smallest RMSE (root mean square error). The results showed that all spatial weights gave the same good GSTAR(1:1) model because they had almost the same RMSE value. Thus, this model can be used to forecast the amount of rainfall for the period January 2019-December 2019. The forecast results show that for Sintang Station and Melawi Station, the highest amount of rainfall is estimated to occur in March 2019 and the lowest will occur in August 2019. Meanwhile, for Ketapang Station, the highest amount of rainfall is estimated to occur in January 2019 and the lowest will occur in September 2019.