Advanced spatio temporal modeling with geographically and temporally weighted spline regression (GTWSR) for strategic food price forecasting in Indonesia

This study proposes an advanced spatio-temporal framework to forecast strategic food commodity prices in Indonesia using Geographically and Temporally Weighted Spline Regression (GTWSR), a nonparametric extension of GTWR designed to capture nonlinear spatio temporal effects. Monthly data from the Strategic Food Price Information Center (SFPIC) and Statistics Indonesia (BPS), covering eight key commodities and the Farmer Price Index across 34 provinces (January 2022-August 2024), were analyzed through spatial distance measurement, bandwidth optimization, local parameter estimation, and statistical validation. The GTWSR model demonstrated strong predictive performance (overall accuracy: R² = 91.61 %, RMSE = 1.22, MAE = 0.94, MAPE = 3.7 %), with rice and garlic achieving the highest accuracy, while red and cayenne chili showed greater errors due to price volatility. Spatial disparities were evident, as eastern provinces such as Papua, Maluku, and East Nusa Tenggara consistently faced higher prices compared to western regions. These findings underscore the need for region-specific interventions to strengthen logistics and stabilize horticultural supply chains. Limitations include reliance on monthly aggregated data, the temporal scope ending in 2024, and dependence on secondary datasets, which may affect replication and long-term applicability.