If the residuals are not within the bands, then some correlations exist, and the model should be improved.įirst, perform a partition on the data. If these plots are in the band of UCL and LCL, then the residuals are random and the model is adequate.ĥ. When a model is fit using the ARIMA method, XLMiner displays the ACF and PACF plots for residuals. The model is fit using the ARIMA (Autoregressive Integrated Moving Average) method.Ĥ. If the ACF and PACF plots are the same, then the same model can be used for both sets.ģ. If the results are in synch, then the model can be fit. Exploratory techniques are applied to both the Training and Validation Sets.
The data is partitioned into two sets with 60% of the data assigned to the Training Set and 40% assigned to the Validation Set.Ģ. Typically the following steps are performed in a time series analysis.ġ.
This data set contains the average income of tax payers by state. On the XLMiner ribbon, from the Applying Your Model tab, select Help - Examples, then Forecasting/Data Mining Examples and open the example data set, Income.xlsx. The following examples illustrate how XLMiner can be used to explore the data to uncover trends and seasonalities.
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