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  • 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, 312 KB, PDF document

    Embargo ends: 17/04/21

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

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A Simple Combination of Univariate Models

Research output: Contribution to journalJournal article

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A Simple Combination of Univariate Models. / Petropoulos, Fotios; Svetunkov, Ivan.

In: International Journal of Forecasting, Vol. 36, No. 1, 01.01.2020, p. 110-115.

Research output: Contribution to journalJournal article

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Petropoulos, Fotios ; Svetunkov, Ivan. / A Simple Combination of Univariate Models. In: International Journal of Forecasting. 2020 ; Vol. 36, No. 1. pp. 110-115.

Bibtex

@article{2431dd3fd2c44dd191ac6dd90eeee8ec,
title = "A Simple Combination of Univariate Models",
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.",
keywords = "M4-competition, ETS, ARIMA, Theta method, Complex exponential smoothing, Median combination",
author = "Fotios Petropoulos and Ivan Svetunkov",
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",
year = "2019",
month = "4",
day = "17",
doi = "10.1016/j.ijforecast.2019.01.006",
language = "English",
volume = "36",
pages = "110--115",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier Science B.V.",
number = "1",

}

RIS

TY - JOUR

T1 - A Simple Combination of Univariate Models

AU - Petropoulos, Fotios

AU - Svetunkov, Ivan

N1 - 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

PY - 2019/4/17

Y1 - 2019/4/17

N2 - 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.

AB - 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.

KW - M4-competition

KW - ETS

KW - ARIMA

KW - Theta method

KW - Complex exponential smoothing

KW - Median combination

U2 - 10.1016/j.ijforecast.2019.01.006

DO - 10.1016/j.ijforecast.2019.01.006

M3 - Journal article

VL - 36

SP - 110

EP - 115

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 1

ER -