To handle covariance and avoid over-fitting with so many features, we used a series of feature selection and dimensionality reduction techniques fed into a ridge regression to create the models.
we filtered the features (which were already restricted to those present in at least 50% of the forecasting tasks) to those with a small linear correlation with the outcome, defined as having a family-wise error rate < 60 (Toothaker, 1993).