Apply Time Series Models

The Apply Time Series Models procedure loads existing time series models from an external file and applies them to the active dataset. You can use this procedure to obtain forecasts for series for which new or revised data are available, without rebuilding your models. Models are generated using the Time Series Modeler procedure.

Example. You are an inventory manager with a major retailer, and responsible for each of 5,000 products. You've used the Expert Modeler to create models that forecast sales for each product three months into the future. Your data warehouse is refreshed each month with actual sales data which you'd like to use to produce monthly updated forecasts. The Apply Time Series Models procedure allows you to accomplish this using the original models, and simply reestimating model parameters to account for the new data.

Statistics. Goodness-of-fit measures: stationary R-square, R-square (R 2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), maximum absolute error (MaxAE), maximum absolute percentage error (MaxAPE), normalized Bayesian information criterion (BIC). Residuals: autocorrelation function, partial autocorrelation function, Ljung-Box Q.

Plots. Summary plots across all models: histograms of stationary R-square, R-square (R 2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), maximum absolute error (MaxAE), maximum absolute percentage error (MaxAPE), normalized Bayesian information criterion (BIC); box plots of residual autocorrelations and partial autocorrelations. Results for individual models: forecast values, fit values, observed values, upper and lower confidence limits, residual autocorrelations and partial autocorrelations.

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Apply Time Series Models Data Considerations

Data. Variables (dependent and independent) to which models will be applied should be numeric.

Assumptions. Models are applied to variables in the active dataset with the same names as the variables specified in the model. All such variables are treated as time series, meaning that each case represents a time point, with successive cases separated by a constant time interval.

  • Forecasts. For producing forecasts using models with independent (predictor) variables, the active dataset should contain values of these variables for all cases in the forecast period. If model parameters are reestimated, then independent variables should not contain any missing values in the estimation period.

Defining Dates

The Apply Time Series Models procedure requires that the periodicity, if any, of the active dataset matches the periodicity of the models to be applied. If you're simply forecasting using the same dataset (perhaps with new or revised data) as that used to the build the model, then this condition will be satisfied. If no periodicity exists for the active dataset, you will be given the opportunity to navigate to the Define Dates dialog box to create one. If, however, the models were created without specifying a periodicity, then the active dataset should also be without one.

To Apply Models

This feature requires the Forecasting option.

  1. From the menus choose:

    Analyze > Forecasting > Apply Traditional Models...

  2. Enter the file specification for a model file or click Browse and select a model file (model files are created with the Time Series Modeler procedure).

Optionally, you can:

Model Parameters and Goodness of Fit Measures

Load from model file. Forecasts are produced using the model parameters from the model file without reestimating those parameters. Goodness of fit measures displayed in output and used to filter models (best- or worst-fitting) are taken from the model file and reflect the data used when each model was developed (or last updated). With this option, forecasts do not take into account historical data--for either dependent or independent variables--in the active dataset. You must choose Reestimate from data if you want historical data to impact the forecasts. In addition, forecasts do not take into account values of the dependent series in the forecast period--but they do take into account values of independent variables in the forecast period. If you have more current values of the dependent series and want them to be included in the forecasts, you need to reestimate, adjusting the estimation period to include these values.

Reestimate from data. Model parameters are reestimated using the data in the active dataset. Reestimation of model parameters has no effect on model structure. For example, an ARIMA(1,0,1) model will remain so, but the autoregressive and moving-average parameters will be reestimated. Reestimation does not result in the detection of new outliers. Outliers, if any, are always taken from the model file.

  • Estimation Period. The estimation period defines the set of cases used to reestimate the model parameters. By default, the estimation period includes all cases in the active dataset. To set the estimation period, select Based on time or case range in the Select Cases dialog box. Depending on available data, the estimation period used by the procedure may vary by model and thus differ from the displayed value. For a given model, the true estimation period is the period left after eliminating any contiguous missing values, from the model's dependent variable, occurring at the beginning or end of the specified estimation period.

Forecast Period

The forecast period for each model always begins with the first case after the end of the estimation period and goes through either the last case in the active dataset or a user-specified date. If parameters are not reestimated (this is the default), then the estimation period for each model is the set of cases used when the model was developed (or last updated).

  • First case after end of estimation period through last case in active dataset. Select this option when the end of the estimation period is prior to the last case in the active dataset, and you want forecasts through the last case.
  • First case after end of estimation period through a specified date. Select this option to explicitly specify the end of the forecast period. Enter values for all of the cells in the Date grid.

    If no date specification has been defined for the active dataset, the Date grid shows the single column Observation. To specify the end of the forecast period, enter the row number (as displayed in the Data Editor) of the relevant case.

    The Cycle column (if present) in the Date grid refers to the value of the CYCLE_ variable in the active dataset.