The Box-Jenkins approach to time series modelling consists of extracting predictable movements (or patterns) from the observed data through a series of iterations. The univariate Box-Jenkins method is purely a forecasting tool; no explanation is offered in that there are no regressor-type variables. The Box-Jenkins approach follows a three phase procedure:Model identification: a particular category of Box-Jenkins (B-J) model is identified by using various statistics computed from an analysis of the historical data.Model estimation and verification: once identified, the ``best model'' is estimated such that the fitted values come as close as possible to capturing the pattern exhibited by the actual data.Forecasting: the final model is used to forecast the time series and to develop confidence intervals that measure the uncertainty associated with the forecast.
CITATION STYLE
Aljandali, A. (2017). The Box-Jenkins Methodology (pp. 59–79). https://doi.org/10.1007/978-3-319-56481-4_3
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