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Discrete forecast reconciliation.

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Title: Discrete forecast reconciliation.
Authors: Zhang, Bohan1 (AUTHOR), Panagiotelis, Anastasios2 (AUTHOR), Kang, Yanfei1 (AUTHOR) yanfeikang@buaa.edu.cn
Source: European Journal of Operational Research. Oct2024, Vol. 318 Issue 1, p143-153. 11p.
Subjects: Quadratic programming, Assignment problems (Programming), Time series analysis, Computational complexity, Forecasting
Abstract: This paper presents a formal framework and proposes algorithms to extend forecast reconciliation to discrete-valued data, including low counts. A novel method is introduced based on recasting the optimisation of scoring rules as an assignment problem, which is solved using quadratic programming. The proposed framework produces coherent joint probabilistic forecasts for count hierarchical time series. Two discrete reconciliation algorithms are also proposed and compared against generalisations of the top-down and bottom-up approaches for count data. Two simulation experiments and two empirical examples are conducted to validate that the proposed reconciliation algorithms improve forecast accuracy. The empirical applications are forecasting criminal offences in Washington D.C. and product unit sales in the M5 dataset. Compared to benchmarks, the proposed framework shows superior performance in both simulations and empirical studies. • Presents forecast reconciliation framework for discrete hierarchical time series. • Proposed DFR algorithm optimises the penalised Brier Score. • Optimisation cast as assignment solved using Quadratic Programming. • Second algorithm handles computational complexity of larger hierarchies. • Simulations and applications show the effectiveness of our framework. [ABSTRACT FROM AUTHOR]
Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 177843441
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PubTypeId: academicJournal
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  Data: Discrete forecast reconciliation.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Bohan%22">Zhang, Bohan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Panagiotelis%2C+Anastasios%22">Panagiotelis, Anastasios</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kang%2C+Yanfei%22">Kang, Yanfei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yanfeikang@buaa.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Operational+Research%22">European Journal of Operational Research</searchLink>. Oct2024, Vol. 318 Issue 1, p143-153. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Quadratic+programming%22">Quadratic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Assignment+problems+%28Programming%29%22">Assignment problems (Programming)</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+complexity%22">Computational complexity</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper presents a formal framework and proposes algorithms to extend forecast reconciliation to discrete-valued data, including low counts. A novel method is introduced based on recasting the optimisation of scoring rules as an assignment problem, which is solved using quadratic programming. The proposed framework produces coherent joint probabilistic forecasts for count hierarchical time series. Two discrete reconciliation algorithms are also proposed and compared against generalisations of the top-down and bottom-up approaches for count data. Two simulation experiments and two empirical examples are conducted to validate that the proposed reconciliation algorithms improve forecast accuracy. The empirical applications are forecasting criminal offences in Washington D.C. and product unit sales in the M5 dataset. Compared to benchmarks, the proposed framework shows superior performance in both simulations and empirical studies. • Presents forecast reconciliation framework for discrete hierarchical time series. • Proposed DFR algorithm optimises the penalised Brier Score. • Optimisation cast as assignment solved using Quadratic Programming. • Second algorithm handles computational complexity of larger hierarchies. • Simulations and applications show the effectiveness of our framework. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.ejor.2024.05.024
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 143
    Subjects:
      – SubjectFull: Quadratic programming
        Type: general
      – SubjectFull: Assignment problems (Programming)
        Type: general
      – SubjectFull: Time series analysis
        Type: general
      – SubjectFull: Computational complexity
        Type: general
      – SubjectFull: Forecasting
        Type: general
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      – TitleFull: Discrete forecast reconciliation.
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            NameFull: Zhang, Bohan
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            NameFull: Kang, Yanfei
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            – D: 01
              M: 10
              Text: Oct2024
              Type: published
              Y: 2024
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              Value: 318
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