Application of air quality combination forecasting to Bogota

• 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.

Ämnesord

SAMHÄLLSVETENSKAP  -- Ekonomi och näringsliv (hsv//swe)

SOCIAL SCIENCES  -- Economics and Business (hsv//eng)

Nyckelord

Air quality forecasting

Bogota

Forecast combination

Neural networks

MISSING VALUES

POLLUTION

IMPUTATION

GREECE

MODEL

SO2

Environmental Sciences

Meteorology & Atmospheric Sciences

ENVIRONMENTAL SCIENCES

METEOROLOGY & ATMOSPHERIC SCIENCES

Publikations- och innehållstyp

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