Forecasting Food Prices in East Java Using Stacking Ensemble Learning via K-MEANS
Keywords:
clustering, ensemble, food commodity, price, time-seriesAbstract
Food commodities are essential in developing countries, such as Indonesia. The government rules the food commodity prices in every province. Yet, somehow, there are some issues in certain provinces. Data science and statistics techniques can help the government control food commodity prices. The proposed method applied to predict the commodity price index in East Java is the STACKEL K-MEANS method. This proposed method is a collaborative framework that utilizes cluster analysis and stacking ensemble learning to predict data. Cluster analysis is performed first, using the distance that suits time series data, which is Dynamic Time Warping. Two clusters are formed from each commodity (Rice, Oil, and Flour). Then, the stacking model consists of a base learner and a meta learner. The base learner models used are Ridge Regression, Random Forest, and Support Vector Regression, while the meta learner is Light Gradient Boosting Method. To optimize the parameter, we used a grid search. Following the evaluation process, we compare the proposed method with auto ARIMA from Python. In training and testing data, The proposed method yields superior results to the ARIMA model across all three error metrics: MAPE, MAE, and RMSE. The following scores for flour commodities are 0.042% compared to 0.328%, 4.715 compared to 37.57, and 6.34 compared to 523.99. For rice commodities, the scores are 0.261% compared to 0.392%, 31.585 compared to 48.142, and 41.92 compared to 56.068 For oil commodities, the scores are 0.185% compared to 0.250%, 33.02 compared to 47.571, and 39.35 compared to 56.060.
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