Combining forecasts for universally optimal performance

Combining forecasts

To improve forecasting accuracy, combine forecasts derived from methods that differ substantially and draw from different sources of information. When feasible, use five or more methods. Use formal procedures to combine forecasts: An equal-weights rule offers a reasonable starting point, and a trimmed mean is desirable if you combine forecasts resulting from five or more methods. Use different ...

Conditionally Optimal Weights and Forward-Looking Approaches to Combining Forecasts

In applied forecasting, there is a trade-off between in-sample fit and out-ofsample forecast accuracy. Parsimonious model specifications typically outperform richer model specifications. Consequently, there is often predictable information in forecast errors that is difficult to exploit. However, we show how this predictable information can be exploited in forecast combinations. In this case, o...

Combining Forecasts using Clustering

Given sales forecasts for a set of items along with the standard deviation associated with each forecast, we propose a new method of combining forecasts using the concepts of clustering. Clusters of items are identified based on similarity in their sales forecasts and then a common forecast (or combined forecast) is computed for each cluster of items. The objective of clustering is to minimize ...

Combining Economic Forecasts

Combining Probability Forecasts

Linear pooling is by the far the most popular method for combining probability forecasts. However, any nontrivial weighted average of two or more distinct, calibrated probability forecasts is necessarily uncalibrated and lacks sharpness. In view of this, linear pooling requires recalibration, even in the ideal case in which the individual forecasts are calibrated. Toward this end, we propose a ...