A Comparison of Model Averaging Methods in Foreign Exchange Prediction

Pedro Domingos

Statistical learning theory predicts, and empirical results confirm, that averaging the predictions of several learned models will often result in higher accuracies than using only the single "best" model. However, the averaging methods typically used in practice (e.g., assigning uniform weights to all models) differ from the theoretical Bayesian optimum, which is to weight each model by its posterior probability. In this extended abstract, uniform and Bayesian weighting are compared on a practical task, some problems of the latter are identified, and a new heuristic weighting scheme is proposed, leading to improved performance.

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