Arbitrage of forecasting experts

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Vitor Cerqueira (Author) Other; Luís Torgo (Author) FCUP; Fábio Pinto (Author) Other; Carlos Soares (Author) FEUP

Journal

Title: Machine Learning

Vol. 108 No. 6

Pages: 913-944

ISSN: 0885-6125

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 24 Citations

Scopus - 30 Citations

Other information

Authenticus ID: P-00Q-044

DOI: 10.1007/s10994-018-05774-y

Abstract (EN): Forecasting is an important task across several domains. Its generalised interest is related to the uncertainty and complex evolving structure of time series. Forecasting methods are typically designed to cope with temporal dependencies among observations, but it is widely accepted that none is universally applicable. Therefore, a common solution to these tasks is to combine the opinion of a diverse set of forecasts. In this paper we present an approach based on arbitrating, in which several forecasting models are dynamically combined to obtain predictions. Arbitrating is a metalearning approach that combines the output of experts according to predictions of the loss that they will incur. We present an approach for retrieving out-of-bag predictions that significantly improves its data efficiency. Finally, since diversity is a fundamental component in ensemble methods, we propose a method for explicitly handling the inter-dependence between experts when aggregating their predictions. Results from extensive empirical experiments provide evidence of the method's competitiveness relative to state of the art approaches. The proposed method is publicly available in a software package.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 32