A Simple Combination of Univariate Models

Petropoulos, Fotios and Svetunkov, Ivan (2020) A Simple Combination of Univariate Models. International Journal of Forecasting, 36 (1). pp. 110-115. ISSN 0169-2070

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Abstract

This paper describes the approach that we implemented for producing the point forecasts and prediction intervals for our M4-competition submission. The proposed simple combination of univariate models (SCUM) is a median combination of the point forecasts and prediction intervals of four models, namely exponential smoothing, complex exponential smoothing, automatic autoregressive integrated moving average and dynamic optimised theta. Our submission performed very well in the M4-competition, being ranked 6 th for the point forecasts (with a small difference compared to the 2 nd submission) and prediction intervals and 2 nd and 3 rd for the point forecasts of the weekly and quarterly data respectively.

Item Type:

Journal Article

Journal or Publication Title:

International Journal of Forecasting

Additional Information:

This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, 36, 1, 2019 DOI: 10.1016/j.ijforecast.2019.01.006

Uncontrolled Keywords:

/dk/atira/pure/subjectarea/asjc/1400/1403

Subjects:

?? m4-competitionetsarimatheta methodcomplex exponential smoothingmedian combinationbusiness and international management ??

Departments:

Lancaster University Management School > Management Science

ID Code:

131943

Deposited By:

ep_importer_pure

Deposited On:

28 Mar 2019 16:29

Refereed?:

Yes

Published?:

Published

Last Modified:

16 Apr 2024 01:09

URI:

https://eprints.lancs.ac.uk/id/eprint/131943