Model selection for weakly dependent time series forecasting

TL;DR

This paper introduces a two-step model selection procedure for weakly dependent stationary time series, combining machine learning and model selection paradigms, with theoretical guarantees for various predictive models.

Contribution

It proposes a novel two-step approach for selecting predictors in weakly dependent time series, providing oracle inequalities for different observation types and models.

Findings

Risk of selected predictor close to the best in all models

Applicable to linear, neural network, and non-parametric autoregressive models

Provides theoretical guarantees for model selection in weakly dependent processes

Abstract

Observing a stationary time series, we propose a two-step procedure for the prediction of the next value of the time series. The first step follows machine learning theory paradigm and consists in determining a set of possible predictors as randomized estimators in (possibly numerous) different predictive models. The second step follows the model selection paradigm and consists in choosing one predictor with good properties among all the predictors of the first steps. We study our procedure for two different types of bservations: causal Bernoulli shifts and bounded weakly dependent processes. In both cases, we give oracle inequalities: the risk of the chosen predictor is close to the best prediction risk in all predictive models that we consider. We apply our procedure for predictive models such as linear predictors, neural networks predictors and non-parametric autoregressive.