Nonparametric sequential prediction for stationary processes

Abstract

We study the problem of finding an universal estimation scheme $h_n:{\mathbb{R}}^n\to \mathbb{R}$, $n=1,2,...$ which will satisfy \lim_{t\rightarrow\infty}{\frac{1}{t}}\sum_{i=1}^t|h_ i(X_0,X_1,...,X_{i-1})-E(X_i|X_0,X_1,...,X_{i-1})|^p=0 a.s. for all real valued stationary and ergodic processes that are in $L^p$. We will construct a single such scheme for all $1<p\le \infty$, and show that for $p=1$ mere integrability does not suffice but $L{\log }^{+}L$ does.

Published in at http://dx.doi.org/10.1214/10-AOP576 the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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• Weakly Convergent Nonparametric Forecasting of Stationary Time Series
• Limits to consistent on-line forecasting for ergodic time series
• On estimating the memory for finitarily Markovian processes
• Forward estimation for ergodic time series
• Inferring the conditional mean
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Cited by in corpus (1)

• A consistent deterministic regression tree for non-parametric prediction of time series