Prediction of ozone peaks by mixture models

This paper applies recent developments of mixture models to the prediction of peak episodes of ozone in Lyon (a major French city). Forecasting for the next day should be available at 14:00 h GMT. One day ahead prediction of such events allow public authorities to warn the population of the danger of outdoor exercises and activities. Compared to a standard discrimination problem, the database has many unusual characteristics among which missing measurements and missing decisions. All this peculiarities are well taken into account by mixture models, which additionally allow class densities to have complex shapes and decision boundaries to be non-linear. The proposed approach is compared to the persistence method, which consists in forecasting the same decision as the day before. The results show that mixture models outperform this simple predictor and offer the advantage of properly handling the missing labels and data.