Author(s): V. Anh, K. Lunney, P. Best, G. Johnson, M. Azzi & H. Duc
This paper describes a fractional autoregressive model and a multivariate
causality model for prediction of maximum daily Lidcombe ozone
The models accommodate long-range dependence, which is an
important aspect of concentration time series.
It is found that morning wind
speed, temperature, extent and ozone measured at 11 am contain information
which can be used to improve the forecasts of univariate models which rely on
the history of the maximum daily ozone series alone.
With these factors
incorporated, the resulting causality model gives a much improved performance
on predicting ozone episodes.
Size: 863 kb
Paper DOI: 10.2495/AIR960161
the Full Article
This article is part of the WIT OpenView scheme and you can download the full text Adobe PDF article for FREE by clicking the 'Openview' icon below.
this page to a colleague.