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Aggregation of Adaptive Forecasting Algorithms Under Asymmetric Loss Function

  • Alexey RomanenkoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9047)

Abstract

The paper deals with applying the strong aggregating algorithm to games with asymmetric loss function. A particular example of such games is the problem of time series forecasting where specific losses from under-forecasting and over-forecasting may vary considerably. We use the aggregating algorithm for building compositions of adaptive forecasting algorithms. The paper specifies sufficient conditions under which a composition based on the aggregating algorithm performs as well as the best of experts. As a result, we find a theoretical bound for the loss process of a given composition under asymmetric loss function. Finally we compare the composition based on the aggregating algorithm to other well-known compositions in experiments with real data.

Keywords

Aggregating algorithm Time series forecasting Asymmetric loss function 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Moscow Institute of Physics and TechnologyMoscow RegionRussian Federation

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