- Related AI/ML Methods: Ensemble Common Fit, Bagging Nonlinear Fit Bootstrap
- Related Traditional Methods: Quick Autoeconometrics, Custom Econometrics
Using an ensemble learning approach, this model computes thousands of possible nonlinear and interaction models (suitable for time-series data for pattern recognition); it calibrates the best model with the training dataset and forecasts outcomes using the testing dataset (Figure 9.68).
Simply enter the variables you need to classify and enter the number of clusters desired. For instance, the required model inputs look like the following:
Figure 9.68: AI/ML Ensemble Learning Complex Fit
The results illustrate the complex ensemble model where thousands of combinations of linear, nonlinear, interacting, time-series (lags, rate, and differences), and mixed models were tested, and the best model is shown. The models are selected based on the independent variables’ p-values ≤ 0.10 and ranked by Adjusted R-squares. A stricter p-value can be entered if required.