Home > Research > Publications & Outputs > A Simple Combination of Univariate Models

Research

Associated organisational units

Electronic data

  • IJF 2019 SCUM (post-print)

    Rights statement: 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

    Accepted author manuscript, 313 KB, PDF document

    Embargo ends: 17/04/21

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

Keywords

View graph of relations

A Simple Combination of Univariate Models

Research output: Contribution to journal › Journal article

Published
<mark>Journal publication date</mark>1/01/2020
<mark>Journal</mark>International Journal of Forecasting
Issue number1
Volume36
Number of pages6
Pages (from-to)110-115
Publication statusPublished
Early online date17/04/19
Original languageEnglish

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.

Bibliographic note

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

Related research outputs

Related activities