@misc{eprints10421,
           month = {Mei},
           title = {PEMODELAN TIME SERIES DAN PERAMALAN
MENGGUNAKAN METODE AUTOREGRESSIVE
MOVING AVERAGE (ARIMA) DAN RANDOM WALK},
          author = {1117031022 Faiga Kharimah},
         address = {UNIVERSITAS LAMPUNG},
       publisher = {FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM},
            year = {2015},
             url = {http://digilib.unila.ac.id/10421/},
        abstract = {ABSTRACT

Autoregressive integrated moving average (ARIMA) is a combination of
Autoregressive (AR) model and Moving Average (MA) model. The ARIMA
model has orde (0,1,0) is called RandomWalk model. The ARIMA model using
past and present value to produce short-term forecasting. The purpose of this
research is to determine the best ARIMA model for forecasting the Consumer
Price Index (CPI) and health comodities price index Bandar Lampung city in the
period January to June 2014. The ARIMA model has assumption that the series
data are stationary. The CPI and health comodities price index of Bandar
Lampung is not stationary, then we apllied differencing to make the data
stationary. To find the best model ARIMA, first we check the stationary data by
using time series plot, Autocorrelation Function (ACF), and unitroot test. Then the
time series model was found by using ACF and Partial Autocorrelations Function
(PACF). The best model was found by using criteria Mean Square Error (MSE),
Akaike?s Information Criterion (AIC) and Bayesian Information Criterion (BIC).
The best model is ARIMA (1,1,0) for CPI and ARIMA (0,1,0) for health
comodities price index.
Key Word : time series, forecasting, CPI, ARIMA}
}