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Online Forecast Combination for Dependent Heterogeneous Data


Type

Working Paper

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Authors

Sancetta, Alessio 

Abstract

This paper studies a procedure to combine individual forecasts that achieve theoretical optimal performance. The results apply to a wide variety of loss functions and no conditions are imposed on the data sequences and the individual forecasts apart from a tail condition. The theoretical results show that the bounds are also valid in the case of time varying combination weights, under specific conditions on the nature of time variation. Some experimental evidence to confirm the results is provided.

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Keywords

Forecast Combination, Model Selection, Multiplicative Update, Non-asymptotic Bound, On-line Learning

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Publisher

Faculty of Economics

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