Prediction of quantiles by statistical learning and application to GDP forecasting

Abstract : In this paper, we tackle the problem of prediction and confidence intervals for time series using a statistical learning approach and quantile loss functions. In a first time, we show that the Gibbs estimator (also known as Exponentially Weighted aggregate) is able to predict as well as the best predictor in a given family for a wide set of loss functions. In particular, using the quantile loss function of Koenker and Bassett (1978), this allows to build confidence intervals. We apply these results to the problem of prediction and confidence regions for the French Gross Domestic Product (GDP) growth, with promising results.
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Communication dans un congrès
J.-G. Ganascia, P. Lenca and J.-M. Petit. The Fifteenth International Conference on Discovery Science (DS 2012), Oct 2012, Lyon, France. Springer, 7569, pp.22-36, 2012, Lecture Notes in Artificial Intelligence
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https://hal.archives-ouvertes.fr/hal-00671982
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Soumis le : lundi 6 août 2012 - 15:36:16
Dernière modification le : lundi 29 mai 2017 - 14:21:42
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  • HAL Id : hal-00671982, version 3
  • ARXIV : 1202.4294

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Pierre Alquier, Xiaoyin Li. Prediction of quantiles by statistical learning and application to GDP forecasting. J.-G. Ganascia, P. Lenca and J.-M. Petit. The Fifteenth International Conference on Discovery Science (DS 2012), Oct 2012, Lyon, France. Springer, 7569, pp.22-36, 2012, Lecture Notes in Artificial Intelligence. <hal-00671982v3>

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