期刊
ATMOSPHERIC ENVIRONMENT
卷 89, 期 -, 页码 22-28出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.atmosenv.2014.02.015
关键词
Air quality forecasting; Bogota; Forecast combination; Neural networks
资金
- Knut and Alice Wallenberg Foundation
- Swedish International Development Cooperation Agency (SIDA)
- PDD Program at the Universidad de los Andes
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. (C) 2014 Elsevier Ltd. All rights reserved.
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