@phdthesis{digilib56272,
           month = {December},
           title = {PENERAPAN METODE ARIMA DAN BAYESIAN STRUCTURAL TIME SERIES UNTUK MEMPREDIKSIKAN INFLASI DI INDO},
          school = {UIN SUNAN KALIJAGA YOGYAKARTA},
          author = {NIM.: 18106010011 Siti Wulandari},
            year = {2022},
            note = {Pembimbing: Sri Utami Zuliana, S.Si., M.Sc., Ph.D},
        keywords = {Peramalan (Forecasting), Stasioneritas, Inflasi},
             url = {https://digilib.uin-suka.ac.id/id/eprint/56272/},
        abstract = {Statistics cannot be separated from everyday life because it is useful in scientific
research and knowledge. By using statistics, we can predict an event that will
occur in the future that will come by predicting. The methods commonly used to
forecasting is a time series method. In the data time series method used for forecasting
must be stationary, if the data to be used is not stationary, it requires differencing
so that the data is stationary. In this study, the model that can be used is the ARIMA
(Autoregressive Integrated Moving Average) and BSTS (Bayesian Structural Time
Series) models. The aim this research is to apply the ARIMA (Autoregressive Integrated
Moving Average) and BSTS (Bayesian Structural Time Series), obtained the
model best, and know the results of forecasting on inflation data. Research methods
that used is the quantitative method.
In this study the modeling process of ARIMA (Autoregressive Integrated Moving
Average) and BSTS (Bayesian Structural Time Series) using software R version
4.10. The data used in this research is inflation in Indonesia in the period January
2015 to December 2021. The results of this study indicate- an that from the
ARIMA (Autoregressive Integrated Moving Average) method and BSTS (Bayesian
Structural Time Series) is the best model for forecasting BSTS model [12] with a
RMSE (Root Mean Square Error) value of 0.043\%. Which can be concluded that
the BSTS model [12] is good to use in forecasting}
}