Study of Clustering Time Series Forecasting Model for Provincial Grouping in Indonesia Based on Rice Price

Abstract

Most indonesians consume rice as the main staple. The high low price of rice has an impact on farmers and communities, especially those who cannot afford it. Rice price forecasting is one of the important information to be considered for future rice prices. The data used is secondary data sourced from bps publication, Rural Consumer Price Statistics: Food Group, from January 2008 to December 2019 for 32 provinces in Indonesia. Time series  modeling and forecasting is usually done on a single variable using ARIMA. however, modeling becomes inefficient if there are many variables, so clustering time series analysis is performed using correlation distance with the clustering method of average linkage hierarchy. Cluster level ARIMA modeling with 4 clusters provides high efficiency because only by doing 4 times modeling results in accuracy values not much different from individual level modeling. the results obtained by individual-level ARIMA Modeling resulted in an average MAPE of 3.36%, while cluster-level ARIMA modeling with 4 clusters resulted in an average MAPE value of 4.27%, with a second MAPE difference of -0.91%. Formally conducted z test, the results obtained there is no difference between individual-level MAPE and cluster-level MAPE. This means that cluster-level modeling is relatively good and representative.