Work

Nonparametric sequential prediction of time series

Year: 2009

Type: article

Abstract: Time series prediction covers a vast field of everyday statistical applications in medical, environmental and economic domains. In this paper, we develop nonparametric prediction strategies based on the combination of a set of ‘experts’ and show the universal consistency of these strategies under a minimum of conditions. We perform an in-depth analysis of real-world data sets and show that these nonparametric strategies are more flexible, faster and generally outperform ARMA methods in terms of normalised cumulative prediction error.

Source: Journal of nonparametric statistics

DOI: 10.1080/10485250802680730

Authors: Gérard Biau, Kevin Bleakley, László Györfi, György Ottucsák

Institutions: Laboratoire de Statistique Théorique et Appliquée, Université Pierre-et-Marie-Curie, Inserm, Centre de Bio-Informatique, École Nationale Supérieure des Mines de Paris, Institut Curie, Budapest University of Technology and Economics

Language: English

Topic: Forecasting Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W2052774554

FWCI: 4.037

Cites: 39

Cited by: 30

Related to: 10