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It was pointed in an other question that ensemble methods can help to reduce curve fitting. What are your experience with these and which one seems the most appropriate? If I had two forecasters that give reasonably good results. Would it be better to use both and invest half in each (diversification) or use one of the ensemble method?

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Ensemble methods, or ensemble learning are a class of statistical methods that, loosely speaking, operate on many rather than a single instance of the data. Think bootstrapping, but then combine the estimates for an aggregate. The Wikipedia link has more.

Combining two forecasters is something else that is sometimes called pooling forecasts or, more generally, consensus forecast.

The main difference is that the pieces in an ensemble method are related---pooling is from the same class or instance of an estimate---whereas pooled forecast are aggregating over different forecast which may not have any commonality.

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Thanks for the clarification. – Zarbouzou Feb 8 '11 at 13:21
Should I edit the question to make it about consensus forecast? – Zarbouzou Feb 8 '11 at 16:17
Good question, and no clear suggestion. Maybe read up a little and then come back with a more pointed question on consensus forecasts, forecast pooling, ... ? – Dirk Eddelbuettel Feb 8 '11 at 16:22

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