his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Neural network ensemble operators for time series forecasting
Lancaster University Management School, Department of Management Science, Lancaster, United Kingdom.ORCID iD: 0000-0003-0211-5218
Lancaster University Management School, Department of Management Science, Lancaster, United Kingdom.
Lancaster University Management School, Department of Management Science, Lancaster, United Kingdom.
2014 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 41, no 9, p. 4235-4244Article in journal (Refereed) Published
Abstract [en]

The combination of forecasts resulting from an ensemble of neural networks has been shown to outperform the use of a single "best" network model. This is supported by an extensive body of literature, which shows that combining generally leads to improvements in forecasting accuracy and robustness, and that using the mean operator often outperforms more complex methods of combining forecasts. This paper proposes a mode ensemble operator based on kernel density estimation, which unlike the mean operator is insensitive to outliers and deviations from normality, and unlike the median operator does not require symmetric distributions. The three operators are compared empirically and the proposed mode ensemble operator is found to produce the most accurate forecasts, followed by the median, while the mean has relatively poor performance. The findings suggest that the mode operator should be considered as an alternative to the mean and median operators in forecasting applications. Experiments indicate that mode ensembles are useful in automating neural network models across a large number of time series, overcoming issues of uncertainty associated with data sampling, the stochasticity of neural network training, and the distribution of the forecasts. 

Place, publisher, year, edition, pages
Elsevier, 2014. Vol. 41, no 9, p. 4235-4244
Keywords [en]
Combination, Ensembles, Forecasting, Kernel density estimation, Mean, Median, Mode estimation, Neural networks, Time series
National Category
Meteorology and Atmospheric Sciences Information Systems
Identifiers
URN: urn:nbn:se:his:diva-18252DOI: 10.1016/j.eswa.2013.12.011ISI: 000333778000018Scopus ID: 2-s2.0-84893467682OAI: oai:DiVA.org:his-18252DiVA, id: diva2:1402790
Available from: 2020-02-28 Created: 2020-02-28 Last updated: 2020-02-28Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Kourentzes, Nikolaos

Search in DiVA

By author/editor
Kourentzes, Nikolaos
In the same journal
Expert systems with applications
Meteorology and Atmospheric SciencesInformation Systems

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 4 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf