Time-Series Data Modeling for Inflation Forecasting based on Subcategories of Commodity using TSClust Approach as Pre-processing

Budi Utami, Hari Wijayanto, I Made Sumertajaya

Abstract


High and unstable inflation will lead to a decline in the quality of life of the people and the slowing down of the economy of a region. The efforts to control inflation are continuously carried out by both central and local governments. Therefore, a precise and efficient forecasting method is needed to forecast inflation according subcategories of commodity, considering the various movement patterns of each subcategory. This research developed a model to predict inflation according to subcategories of commodity. To determine the pattern and model of each subcategory of commodity, a time-series analysis approach was used to obtain a good and efficient forecasting model in terms of time, effort and cost. The cluster-level model established were ARIMA, ARIMAX, VAR and VARX models. The research shows that the best clustering 35 inflation rate based on the subcategories of commodity were obtained by using Piccolo measure of dissimilarity, resulted in 4 clusters. The best forecasting model varies for each subcategory of commodity, but ARIMAX model shows the smallest RMSE value compared to the other three models.


Keywords


arimax; inflation; tsclust; varx.

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