# Prediction of time series by statistical learning: general losses and fast rates

Pierre Alquier; Xiaoyin Li; Olivier Wintenberger

Dependence Modeling (2013)

- Volume: 1, page 65-93
- ISSN: 2300-2298

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topPierre Alquier, Xiaoyin Li, and Olivier Wintenberger. "Prediction of time series by statistical learning: general losses and fast rates." Dependence Modeling 1 (2013): 65-93. <http://eudml.org/doc/267337>.

@article{PierreAlquier2013,

abstract = {We establish rates of convergences in statistical learning for time series forecasting. Using the PAC-Bayesian approach, slow rates of convergence √ d/n for the Gibbs estimator under the absolute loss were given in a previous work [7], where n is the sample size and d the dimension of the set of predictors. Under the same weak dependence conditions, we extend this result to any convex Lipschitz loss function. We also identify a condition on the parameter space that ensures similar rates for the classical penalized ERM procedure. We apply this method for quantile forecasting of the French GDP. Under additional conditions on the loss functions (satisfied by the quadratic loss function) and for uniformly mixing processes, we prove that the Gibbs estimator actually achieves fast rates of convergence d/n. We discuss the optimality of these different rates pointing out references to lower bounds when they are available. In particular, these results bring a generalization the results of [29] on sparse regression estimation to some autoregression.},

author = {Pierre Alquier, Xiaoyin Li, Olivier Wintenberger},

journal = {Dependence Modeling},

keywords = {Statistical learning theory; time series forecasting; PACBayesian bounds; weak dependence; mixing; oracle inequalities; fast rates; GDP forecasting; statistical learning theory},

language = {eng},

pages = {65-93},

title = {Prediction of time series by statistical learning: general losses and fast rates},

url = {http://eudml.org/doc/267337},

volume = {1},

year = {2013},

}

TY - JOUR

AU - Pierre Alquier

AU - Xiaoyin Li

AU - Olivier Wintenberger

TI - Prediction of time series by statistical learning: general losses and fast rates

JO - Dependence Modeling

PY - 2013

VL - 1

SP - 65

EP - 93

AB - We establish rates of convergences in statistical learning for time series forecasting. Using the PAC-Bayesian approach, slow rates of convergence √ d/n for the Gibbs estimator under the absolute loss were given in a previous work [7], where n is the sample size and d the dimension of the set of predictors. Under the same weak dependence conditions, we extend this result to any convex Lipschitz loss function. We also identify a condition on the parameter space that ensures similar rates for the classical penalized ERM procedure. We apply this method for quantile forecasting of the French GDP. Under additional conditions on the loss functions (satisfied by the quadratic loss function) and for uniformly mixing processes, we prove that the Gibbs estimator actually achieves fast rates of convergence d/n. We discuss the optimality of these different rates pointing out references to lower bounds when they are available. In particular, these results bring a generalization the results of [29] on sparse regression estimation to some autoregression.

LA - eng

KW - Statistical learning theory; time series forecasting; PACBayesian bounds; weak dependence; mixing; oracle inequalities; fast rates; GDP forecasting; statistical learning theory

UR - http://eudml.org/doc/267337

ER -

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