Residual-Based Hybrid ARIMA–Prophet for Medium Rice Price Forecasting in Kudus City
DOI:
https://doi.org/10.63158/journalisi.v8i4.1874Keywords:
Hybrid ARIMA-Prophet, Random Forest, Directional Forecasting, PIHPS, Diebold-Mariano TestAbstract
Rice prices as a primary food commodity in Indonesia often fluctuate, affecting food-price stability and public welfare. This study analyzes and forecasts Medium I and Medium II rice prices in Kudus City using ARIMA, Prophet, Hybrid ARIMA–Prophet, Random Forest, and benchmark models. Daily PIHPS price data from 2021–2025 were divided chronologically into 781 training observations from 2021–2023 and 523 testing observations from 2024–2025. Model performance was evaluated using MAE, RMSE, MAPE, R², and directional metrics. Hybrid ARIMA–Prophet achieved MAE of Rp369.68 and MAPE of 2.62% for Medium I, and MAE of Rp467.31 and MAPE of 3.38% for Medium II. However, simpler benchmark models achieved lower price-level errors than the hybrid model. Random Forest showed higher directional Accuracy than Hybrid ARIMA–Prophet, but its low Precision and F1-Score indicate that Accuracy should be interpreted cautiously. The directional analysis also showed a highly imbalanced distribution, with unchanged movements accounting for approximately 94% of test observations. The Diebold–Mariano test, applied only to the regression forecast errors of Hybrid ARIMA–Prophet and Random Forest, showed significant differences for both Medium I and Medium II (p < 0.001). Overall, the findings indicate that more complex models do not necessarily outperform simpler benchmarks for the studied rice-price series.
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