One of the methods which used in forecasting is model of ARIMA
(Autoregressive Integrated Moving Average). Model ARIMA in this
research is model of ARIMA(1,1,1) representing aliance of model of
Autoregressive / AR(1) and model of Moving Average / MA(1) which
passing process of first differencing in order to become stasioner. The
way can be used to analyse model of ARIMA(1,1,1) is by studying
autocorrelation of the model. Forecasting model in this research is
model of ARIMA(1,1,1) told as the best model or appropriate model, if
owning errors swampy forest which free from autocorrelation. At this
research was obtained the stages to detect the existence of
autocorrelation at errors accompanied with example data of model of
ARIMA(1,1,1) with autocorrelation and no autocorrelation so that from
both the example data can be compared and obtained some indication of
existence of autocorrelation at errors model ARIMA. For the indication
of autocorrelation obtained, if is proven of model with autocorrelation
at errors, hence for escade done by transformation at the model to be
owning errors which free from autocorrelation. This transformation can
be done with assumption that errors of model of ARIMA(1,1,1) with
autocorrelation form model of autoregressive (AR) order 1 and fulfill
assumption of OLS. If given assumption do not fulfill, but fulfill with
autocorrelation then must be done by changing obtained model with
more model according to one of them that is freing from
autocorrelation.
(Autoregressive Integrated Moving Average). Model ARIMA in this
research is model of ARIMA(1,1,1) representing aliance of model of
Autoregressive / AR(1) and model of Moving Average / MA(1) which
passing process of first differencing in order to become stasioner. The
way can be used to analyse model of ARIMA(1,1,1) is by studying
autocorrelation of the model. Forecasting model in this research is
model of ARIMA(1,1,1) told as the best model or appropriate model, if
owning errors swampy forest which free from autocorrelation. At this
research was obtained the stages to detect the existence of
autocorrelation at errors accompanied with example data of model of
ARIMA(1,1,1) with autocorrelation and no autocorrelation so that from
both the example data can be compared and obtained some indication of
existence of autocorrelation at errors model ARIMA. For the indication
of autocorrelation obtained, if is proven of model with autocorrelation
at errors, hence for escade done by transformation at the model to be
owning errors which free from autocorrelation. This transformation can
be done with assumption that errors of model of ARIMA(1,1,1) with
autocorrelation form model of autoregressive (AR) order 1 and fulfill
assumption of OLS. If given assumption do not fulfill, but fulfill with
autocorrelation then must be done by changing obtained model with
more model according to one of them that is freing from
autocorrelation.
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