Ensemble Methods for Combining Numerical Predictions in MQL5
Summary
The article explains how to combine numerical predictions from multiple trained models, with MQL5 implementations of several ensemble methods. It begins with a simple average, whose squared error is bounded by the average squared error of the component predictions. Averaging is straightforward and avoids fitting extra parameters, but can be weakened when model quality varies substantially.
Other methods form weighted combinations, including linear regression fitted to component predictions, with additional approaches discussed for different data conditions. The article notes that fitted combinations can overfit, especially on small or noisy datasets, while smoothing approaches may give up some fit on larger, cleaner data. It recommends choosing a method for the dataset and comparing candidates on validation data. The material concerns prediction methodology and implementation; it does not establish that any ensemble improves trading returns.
Key ideas
- A simple average combines model outputs without fitting weights and has a squared-error bound based on component errors.
- Averaging can underuse strong models when component predictive quality differs widely.
- Linear regression can learn weights and an intercept from component model predictions.
- More flexible fitted ensembles can overfit, particularly with limited or noisy data.
- Ensemble choice should be evaluated against dataset characteristics using validation data.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.