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  • Kourentzes 2018 forecast-selection-combination

    Rights statement: This is the author’s version of a work that was accepted for publication in International Journal of Production Economics. 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 Production Economics,209, 2018 DOI: 10.1016/j.ijpe.2018.05.019

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Another look at forecast selection and combination: evidence from forecast pooling

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Another look at forecast selection and combination : evidence from forecast pooling. / Kourentzes, Nikolaos; Barrow, Devon Kennard; Petropoulos, Fotios.

In: International Journal of Production Economics, Vol. 209, 01.03.2019, p. 226-235.

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@article{b481fb1238cd4d419b0397a30b17b8c8,
title = "Another look at forecast selection and combination: evidence from forecast pooling",
abstract = "Forecast selection and combination are regarded as two competing alternatives. In the literature there is substantial evidence that forecast combination is beneficial, in terms of reducing the forecast errors, as well as mitigating modelling uncertainty as we are not forced to choose a single model. However, whether all forecasts to be combined are appropriate, or not, is typically overlooked and various weighting schemes have been proposed to lessen the impact of inappropriate forecasts. We argue that selecting a reasonable pool of forecasts is fundamental in the modelling process and in this context both forecast selection and combination can be seen as two extreme pools of forecasts. We evaluate forecast pooling approaches and find them beneficial in terms of forecast accuracy. We propose a heuristic to automatically identify forecast pools, irrespective of their source or the performance criteria, and demonstrate that in various conditions it performs at least as good as alternative pools that require additional modelling decisions and better than selection or combination.",
keywords = "Forecasting, Model selection, Forecast combination, Forecast Pooling, Cross-validation",
author = "Nikolaos Kourentzes and Barrow, {Devon Kennard} and Fotios Petropoulos",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in International Journal of Production Economics. 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 Production Economics,209, 2018 DOI: 10.1016/j.ijpe.2018.05.019",
year = "2019",
month = mar,
day = "1",
doi = "10.1016/j.ijpe.2018.05.019",
language = "English",
volume = "209",
pages = "226--235",
journal = "International Journal of Production Economics",
issn = "0925-5273",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - Another look at forecast selection and combination

T2 - evidence from forecast pooling

AU - Kourentzes, Nikolaos

AU - Barrow, Devon Kennard

AU - Petropoulos, Fotios

N1 - This is the author’s version of a work that was accepted for publication in International Journal of Production Economics. 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 Production Economics,209, 2018 DOI: 10.1016/j.ijpe.2018.05.019

PY - 2019/3/1

Y1 - 2019/3/1

N2 - Forecast selection and combination are regarded as two competing alternatives. In the literature there is substantial evidence that forecast combination is beneficial, in terms of reducing the forecast errors, as well as mitigating modelling uncertainty as we are not forced to choose a single model. However, whether all forecasts to be combined are appropriate, or not, is typically overlooked and various weighting schemes have been proposed to lessen the impact of inappropriate forecasts. We argue that selecting a reasonable pool of forecasts is fundamental in the modelling process and in this context both forecast selection and combination can be seen as two extreme pools of forecasts. We evaluate forecast pooling approaches and find them beneficial in terms of forecast accuracy. We propose a heuristic to automatically identify forecast pools, irrespective of their source or the performance criteria, and demonstrate that in various conditions it performs at least as good as alternative pools that require additional modelling decisions and better than selection or combination.

AB - Forecast selection and combination are regarded as two competing alternatives. In the literature there is substantial evidence that forecast combination is beneficial, in terms of reducing the forecast errors, as well as mitigating modelling uncertainty as we are not forced to choose a single model. However, whether all forecasts to be combined are appropriate, or not, is typically overlooked and various weighting schemes have been proposed to lessen the impact of inappropriate forecasts. We argue that selecting a reasonable pool of forecasts is fundamental in the modelling process and in this context both forecast selection and combination can be seen as two extreme pools of forecasts. We evaluate forecast pooling approaches and find them beneficial in terms of forecast accuracy. We propose a heuristic to automatically identify forecast pools, irrespective of their source or the performance criteria, and demonstrate that in various conditions it performs at least as good as alternative pools that require additional modelling decisions and better than selection or combination.

KW - Forecasting

KW - Model selection

KW - Forecast combination

KW - Forecast Pooling

KW - Cross-validation

U2 - 10.1016/j.ijpe.2018.05.019

DO - 10.1016/j.ijpe.2018.05.019

M3 - Journal article

VL - 209

SP - 226

EP - 235

JO - International Journal of Production Economics

JF - International Journal of Production Economics

SN - 0925-5273

ER -