Air pollution is one of the most important environmental problems in urban areas, being extremely critical in Mexico City. The main air pollution problem that has been identified in Mexico City metropolitan area is the formation of photochemical smog, primarily ozone. The study and development of modeling methodologies that allow the capturing of time series behavior becomes an important task. The present work aims to develop Fuzzy Inductive Reasoning (FIR) models using the Visual-FIR platform. FIR offers a model-based approach to modeling and predicting either univariate or multivariate time series. Visual-FIR offers an easy-friendly environment to perform this task. In this research, long term prediction of maximum ozone concentration in the centre region of Mexico City metropolitan area is performed. The data were registered every hour and include missing values. Two modeling perspectives are analyzed, i.e. monthly and seasonal models. The results show that the models identified capture the dynamic behavior of ozone contaminant in an accurate manner.