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Forecast PRO expert selector is the quickest way and usually the most accurate way to produce a statistically accurate time series forecast. Each separate entity is separately treated and the most accurate forecasting methodology is selected. So within one product group, one item may be forecasted using a Box Jenkins method and another with more seasonality might use exponential smoothing. Business Intelligence software. SEE YOUR FORECAST IN FORECAST PRO
We have discussed Exponential smoothing before and here we deal with Box Jenkins. However Simple stuff first, Box Jenkins derives its name from George Box and Gwilym Jenkins.
Box Jenkins forecasting is a Mathematical and statistical forecasting model used typically for accurate short-term forecasts of 'well-behaved' data (that shows predictable repetitive cycles and patterns. The first step in developing a Box-Jenkins model is to determine if the data points are stationary and to ascertain what level of seasonality that needs to be modelled
In a time series analysis, Box Jenkins applies autoregressive moving average ARMA and ARIMA models to find the best fit of a time series in time histories in order to produce a forecast. The data sometimes needs to be edited to deal with extreme or missing values or other distortions through the use of functions as log or inverse to achieve stabilization. January 2011 – Book a free forecast.
Many regard Box Jenkins as the most pertinent forecasting methodology owing to its classical decomposition of a series into trend, seasonality and history.
At the model identification stage, our goal is to detect seasonality, if it exists, and to identify the order for the seasonal autoregressive and seasonal moving average terms. For many series, the period is known and a single seasonality term is sufficient. For example, for monthly data we would typically include either a seasonal AR 12 term or a seasonal MA 12 term. For Box-Jenkins models, we do not explicitly remove seasonality before fitting the model. Instead, we include the order of the seasonal terms in the model specification to the ARIMA estimation software. However, it may be helpful to apply a seasonal difference to the data and regenerate the autocorrelation and partial autocorrelation plots. This may help in the model identification of the non-seasonal component of the model. In some cases, the seasonal differencing may remove most or all of the seasonality effect.
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