Time Series Model Using Autoregressive Integrated Moving Average (ARIMA) Method For Inflation In Indonesia - Safwandi (FEBI IAIN Langsa, 2023)
Inflation are related to changes in the price of an item that have the potential to change the market in the short term and create a certain monetary policy. Inflation data tends to fluctuate from time to time, so it is interesting to make useful predictions to provide information on future inflation rates. The consumer price index indicator, which is the best benchmark for inflation, is the main inflation indicator. This research focuses on time series modeling using the autoregressive integrated and moving average (ARIMA) method. Fluctuating data results in the desired model determination and forecasting is carried out so that it is likely to occur in the future. The results showed that inflation predictions for the period January 2010 to December 2022 were obtained using the autoarima model (0,1,1)(1,0,2)[12] with an error value of MAPE 6.61%, RMSE of 0.42 with a p-test level value Î±=5%. From the prediction results, it is obtained that the average in the first quarter is 5.44% in the coming year and gradually decreases with a range of 3% - 4%.