Inflation Forecasting by Commodity Using the Autoreggressive Integrated Moving Average (ARIMA) Method
- 10.2991/aebmr.k.210717.045How to use a DOI?
- forecasting, inflation, Autoreggressive Integrated Moving Average (ARIMA)
The aim of this research is to determine the best Autoregressive Integrated Moving Average (ARIMA) model and its implementation to predict monthly inflation in Indonesia. The data used is the inflation data on the expenditure group of foods, beverages, cigarettes and tobaccos in period January 2010 until December 2019. The method used in this study is the documentation technique. The data analysis technique used is the Autoregressive Integrated Moving Average (ARIMA) which is calculated using the SPSS version 26. The result of this research shows that ARIMA model (12,0,12) is the best model to predict monthly inflation on the expenditure group of foods, beverages, cigarettes and tobaccos in Indonesia for the next period. The results of forecasting 12 months in 2020 with the ARIMA model (12,0,12), in January until April decrease, then for May until August increase while September decrease and in October until December experienced an increase. Therefore, inflation is considered a major problem in the modern economy so that inflationary forecasting can be used in making an economic policy of the coming period which aims to reduce and stabilize price growth.
- © 2021, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Devi Ambar Wati AU - Nurafni Eltivia AU - Ludfi Djajanto PY - 2021 DA - 2021/07/19 TI - Inflation Forecasting by Commodity Using the Autoreggressive Integrated Moving Average (ARIMA) Method BT - Proceedings of 2nd Annual Management, Business and Economic Conference (AMBEC 2020) PB - Atlantis Press SP - 226 EP - 231 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.210717.045 DO - 10.2991/aebmr.k.210717.045 ID - Wati2021 ER -