Application of air quality combination forecasting to Bogota
journal contribution
posted on 2014-06-01, 00:00 authored by Joakim WesterlundJoakim Westerlund, J-P Urbain, J BonillaThe 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.
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Journal
Atmospheric environmentVolume
89Pagination
22-28Location
Amsterdam, The NetherlandsPublisher DOI
ISSN
1873-2844eISSN
1352-2310Language
engPublication classification
C1.1 Refereed article in a scholarly journal, C Journal articleCopyright notice
2014, ElsevierPublisher
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