<![CDATA[Purpose: Accurate forecasting of rice prices is essential to ensure food security and a healthy economy for a country like Indonesia. Problems regarding time-series phenomena, such as trends or seasonality, are problematic for traditional approaches like ARIMA (Autoregressive Integrated Moving Average). This study analyzes the effect of EEMD (Ensemble Empirical Mode Decomposition) combined with time-series data clustering on forecasting accuracy. Methods: From 2009 until 2023, the thirty-two Indonesian provincial rice prices were grouped monthly into time-series clusters using hierarchical clustering, average linkage, and DTW (Dynamic Time Warping). After clusterization, the time series were decomposed using the ensemble EEMD method to extract their IMFs (Intrinsic Mode Functions) and residual components. Each IMF was assigned an ARIMA model. The model forecast was generated by adding all individual estimates. MAPE (Mean Absolute Percentage Error) was used to measure the model's performance. Result: The prices were divided into three clusters with an optimized region. Price changes are well captured through EEMD, where the residual components contributed predominantly to the long-term trends. The validation of the prediction showed MAPE values under 10% for the majority of the provinces, which indicates a relatively accurate prediction. On the other hand, some regions had inaccuracies that were higher than others due to uncontrollable fluctuations. Novelty: This study integrates clustering with EEMD decomposition for monthly rice price forecasting using data from 32 Indonesian provinces from 2009 - 2023, offering a novel approach that improves traditional techniques. The model can capture distinct regional price patterns and provide essential information to policymakers to manage rice supply and price stabilization. Further studies can develop external hybrid models with economic variables.]]>
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