Aggregation of time series predictors, optimality in a locally stationary context

Abstract : This thesis regroups our results on dependent time series prediction. The work is divided into three main chapters where we tackle different problems. The first one is the aggregation of predictors of Causal Bernoulli Shifts using a Bayesian approach. The second one is the aggregation of predictors of what we define as sub-linear processes. Locally stationary time varying autoregressive processes receive a particular attention; we investigate an adaptive prediction scheme for them. In the last main chapter we study the linear regression problem for a general class of locally stationary processes.
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Andrés Sànchez Pérez. Aggregation of time series predictors, optimality in a locally stationary context. Statistics [math.ST]. Télécom ParisTech, 2015. English. ⟨NNT : 2015ENST0051⟩. ⟨tel-01280365⟩

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