BEEF PRICE FORECASTING BASED ON TEMPORAL, SPATIAL AND SPACE-TIME PARAMETER INDICES - Fatimah, Syifa Nurul; Zainnuddin, Ahmad Fuad; Mardiana, Novi; Mukhaiyar, Utriweni (PATTIMURA UNIVERSITY, 2025)
Beef is among the most sought-after commodities in Indonesia, resulting in significant price fluctuations, particularly during religious holidays. These price variations affect inflation and necessitate adjustments in government policies concerning beef distribution and imports. Therefore, it is essential to analyze and predict beef prices using empirical data from regions with the highest beef production and consumption levels. This study aims to examine beef price data through the lenses of temporal, spatial, and space-time dependencies within Java. The methodologies employed in this research include ARIMA, Semivariogram, Kriging, and GSTAR models applied to weekly beef price data from Java. ARIMA is used to analyze and forecast time series data based on past values and past forecast errors. The Semivariogram measures spatial dependence by quantifying how price similarities change with distance. Kriging is a geostatistical interpolation method that predicts price values at unobserved locations based on spatial correlation. GSTAR extends ARIMA by incorporating spatial and temporal dependencies to model interactions across different locations over time. The data used in this study consists of weekly beef price records from major markets across Java, obtained from National Food Agency of Indonesia, from August 2022 to May 2024. The findings of this study reveal that beef price fluctuations in Java are primarily influenced by temporal factors, particularly major religious holidays, rather than by location or a combination of location and time. However, there are spatial variations in beef prices across different observation locations. The best predictive model for forecasting beef prices is the ARIMA model. These results provide valuable insights into the patterns of beef prices based on temporal, spatial, and space-time parameters, offering a robust framework for understanding and anticipating price dynamics in the region.