Combining Forecasts Using Ensemble Learning

TL;DR

This paper explores ensemble learning techniques like Bagging and Boosting to effectively combine individual forecasts, providing theoretical insights and demonstrating improved performance on real-world data.

Contribution

It adapts ensemble learning methods for forecast combination, linking probability elicitation with classification, and shows their effectiveness in improving forecast accuracy.

Findings

Averaging forecasts is equivalent to Bagging, explaining its success.

Boosting produces calibrated and refined combined forecasters.

Proposed methods outperform individual forecasters on real data.

Abstract

The problem of combining individual forecasters to produce a forecaster with improved performance is considered. The connections between probability elicitation and classification are used to pose the combining forecaster problem as that of ensemble learning. With this connection in place, a number of theoretically sound ensemble learning methods such as Bagging and Boosting are adapted for combining forecasters. It is shown that the simple yet effective method of averaging the forecasts is equivalent to Bagging. This provides theoretical insight into why the well established averaging of forecasts method works so well. Also, a nonlinear combination of forecasters can be attained through Boosting which is shown to theoretically produce combined forecasters that are both calibrated and highly refined. Finally, the proposed methods of combining forecasters are applied to the Good Judgment Project data set and are shown to outperform the individual forecasters.