The Geographical Clustering of The Rainfall Stations on Seasonal GSTAR Modeling for Rainfall Forecasting

Abstract

Recent research in time series shows that the data not only have inter-relations with events in the previous time, but also have inter-location linkages. This type of time series data with elements of time and location dependencies are modeled with the Space-Time model. The Space-Time model with heterogeneous research sites is the Generalized Space Time Autoregressive (GSTAR) model. The data that has a seasonal pattern is modeled with seasonal GSTAR by including seasonal elements in the non-seasonal model. In this case, Jember District has 77 rain stations with various regional topography. Based on various characteristics, K-Means cluster analysis is used to obtain optimal rainfall rain stations clusters. This clustering is expected to give better rainfall forecasting result compared with clustering conducted by the Statistics Central of Jember (BPS). The RMSE value can be minimized by including seasonal elements in the model, both in BPS and K-Means clustering. In addition, the K-Means clustering in this study may also reduce the RMSE value of model, both on non-seasonal and seasonal models. The best model for this case is GSTARK-Seasional (1;1) , ie Seasonal GSTAR model on K-Means clustering.