<![CDATA[FVolatile fluctuations in large red chili prices pose a persistent challenge to Indonesia’s food security and regional economic stability, as price shocks directly affect household purchasing power, inflation, and agricultural income. Addressing this issue requires a forecasting framework that captures both spatial interdependence among producing and consuming regions and temporal price dynamics. This study develops an advanced forecasting model for large red chili prices in East Java covering Malang Regency, Banyuwangi Regency, and Surabaya City using the Generalized Space-Time Autoregressive–Seemingly Unrelated Regression (GSTAR-SUR) method. The model integrates the Generalized Least Squares (GLS) approach to enhance parameter estimation efficiency under correlated residuals and applies a partial t-test–based parameter elimination procedure to retain only statistically significant predictors. Compared to traditional univariate time-series approaches such as ARIMA, GSTAR-SUR more effectively captures cross-regional price linkages and residual dependencies, yielding higher forecasting accuracy. The best-performing specification, GSTAR-SUR(3,1)-I(1) with a uniform spatial weighting matrix, achieved RMSE = 1426.73, MAPE = 3.29%, and R² = 0.8482, representing a substantial improvement in precision over conventional GSTAR and ARIMA models. Fourteen-day forecasts reveal region-specific dynamics: a mild downward trend in Malang, an initial rise followed by decline in Banyuwangi, and relative stability in Surabaya. These results demonstrate that the GSTAR-SUR framework can effectively model complex spatio-temporal dependencies in commodity markets and serves as a practical decision-support tool for policymakers in stabilizing food prices, improving distribution strategies, and strengthening agricultural market resilience across East Java.]]>
