Application of air quality combination forecasting to Bogota

Westerlund, Joakim, Urbain, Jean-Pierre and Bonilla, Jorge 2014, Application of air quality combination forecasting to Bogota, Atmospheric environment, vol. 89, pp. 22-28, doi: 10.1016/j.atmosenv.2014.02.015.

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Title Application of air quality combination forecasting to Bogota
Author(s) Westerlund, JoakimORCID iD for Westerlund, Joakim orcid.org/0000-0002-8030-5706
Urbain, Jean-Pierre
Bonilla, Jorge
Journal name Atmospheric environment
Volume number 89
Start page 22
End page 28
Total pages 7
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2014-06
ISSN 1352-2310
1873-2844
Keyword(s) Air quality forecasting
Bogota
Forecast combination
Neural networks
Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Environmental Sciences
Meteorology & Atmospheric Sciences
Environmental Sciences & Ecology
MISSING VALUES
POLLUTION
IMPUTATION
GREECE
MODEL
SO2
Summary The bulk of existing work on the statistical forecasting of air quality is based on either neural networks or linear regressions, which are both subject to important drawbacks. In particular, while neural networks are complicated and prone to in-sample overfitting, linear regressions are highly dependent on the specification of the regression function. The present paper shows how combining linear regression forecasts can be used to circumvent all of these problems. The usefulness of the proposed combination approach is verified using both Monte Carlo simulation and an extensive application to air quality in Bogota, one of the largest and most polluted cities in Latin America. © 2014 Elsevier Ltd.
Language eng
DOI 10.1016/j.atmosenv.2014.02.015
Field of Research 150202 Financial Econometrics
Socio Economic Objective 919999 Economic Framework not elsewhere classified
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30077708

Document type: Journal Article
Collections: Faculty of Business and Law
School of Accounting, Economics and Finance
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