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
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 - 2020/1/1
Y1 - 2020/1/1
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 -