Abstract

This paper develops a new approach to forecast natural gas consumption via ensembles. It combines Bootstrap Aggregation (Bagging), univariate time series forecasting methods and modified regularization routines. A new variant of Bagging is introduced, which uses Maximum Entropy Bootstrap (MEB) and a modified regularization routine that ensures that the data generating process is kept in the ensemble. Monthly natural gas consumption time series from 18 European countries are considered. A comparative, out-of-sample evaluation is conducted up to 12 steps (a year) ahead, using a comprehensive set of competing forecasting approaches. These range from statistical benchmarks to machine learning methods and state-of-the-art ensembles. Several performance (accuracy) metrics are used, and a sensitivity analysis is undertaken. Overall, the new variant of Bagging is flexible, reliable, and outperforms well-established approaches. Consequently, it is suitable to support decision making in the energy and other sectors.

Publication Type:	Article
Additional Information:	© 2022. This article has been accepted for publication in Energy Economics, by Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords:	Forecasting, Natural gas demand, Ensembles, Bagging, Regularization
Subjects:	H Social Sciences > HB Economic Theory T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments:	Bayes Business School > Management

Text - Accepted Version This document is not freely accessible until 15 July 2023 due to copyright restrictions. Available under License Creative Commons Attribution Non-commercial No Derivatives. To request a copy, please use the button below. Request a copy

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