FORECASTING THE CONSUMER PRICE INDEX WITH GENERALIZED SPACE-TIME AUTOREGRESSIVE SEEMINGLY UNRELATED REGRESSION (GSTAR-SUR): COMPROMISE REGION AND TIME

Economic success will provide benefits for improving people’s welfare. An important indicator to determine economic can be seen through inflation by calculating the Consumer Price Index (CPI). CPI is a time series data that influenced elements between locations. The GeneralizedSpace-Time Autoregressive (GSTAR) method suitable applied because it involves of and location (spatiotemporal). problem GSTAR model cannot detect any correlated residuals. was developed into GSTAR-SUR estimate parameters with residuals so produce more efficient estimates. purpose this study best predict six cities in Central Java, namely Cilacap, Purwokerto, Kudus, Surakarta, Semarang, Tegal. used secondary sourced from BPS Java Province. Based on results analysis, formed (11)-I(1) an RMSE value 6.213. Forecasting show next 6 months increase every month each city