Open AccessInflation Forecasting for East Kalimantan Province Using Hybrid Singular Spectrum Analysis- Autoregressive Integrated Moving Average Model
Author(s) -
Melisa Arumsari,
Sri Wahyuningsih,
Meiliyani Siringoringo
Publication year - 2021
Publication title -
jurnal matematika, statistika dan komputasi/jurnal matematika statistik dan komputasi
Language(s) - English
Resource type - Journals
eISSN - 2614-8811
pISSN - 1858-1382
DOI - 10.20956/j.v18i1.14284
Subject(s) - autoregressive integrated moving average , singular spectrum analysis , inflation (cosmology) , mean squared error , mean absolute percentage error , moving average , autoregressive model , econometrics , statistics , forecast error , autoregressive–moving average model , mean absolute error , mathematics , time series , algorithm , singular value decomposition , physics , theoretical physics
The Singular Spectrum Analysis (SSA)-Autoregressive Integrated Moving Average (ARIMA) hybrid method is a good combination of forecasting methods to improve forecasting accuracy and is suitable for economic data that tends to have trend and seasonal patterns, one of which is inflation data. The purpose of this study is to obtain the results of inflation forecasting for East Kalimantan Province in 2021 using the SSA-ARIMA hybrid model. The results of the inflation forecasting for East Kalimantan Province in 2021 using the SSA-ARIMA(1,1,1) hybrid model overall experienced an increase and the highest inflation in 2021 occurred in December of 0.92% with a forecasting accuracy level based on the Root Mean Square Error (RMSE) was 0.069399 and Mean Absolute Percentage Error (MAPE) was 32.61084%