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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Aggregation of Adaptive Forecasting Algorithms Under Asymmetric Loss Function
verfasst von : Alexey Romanenko
Erschienen in: Statistical Learning and Data Sciences
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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.