Data-driven approaches for meteorological time series prediction: A comparative study of the state-of-the-art computational intelligence techniques

With the proliferation of sensor generated weather data, the data-driven modeling for prediction of meteorological time series has gained increasing research interest in current years. The recent advancement in machine learning and artificial intelligence paradigm has made such data analysis process more effective, flexible and sound. This paper attempts to provide a comparative study of the state-of-the art computational intelligence (CI) techniques, which have been successfully applied for meteorological time series prediction purpose. The study has been carried out considering eleven distinct variants of CI techniques, especially based on artificial neural network (ANN), fuzzy logic, Bayesian network (BN) and other probabilistic models. Further, one more hybrid CI technique (SpaFBN), derived from the existing approaches, has been proposed in the present work. All these CI techniques have been empirically studied with respect to a multivariate meteorological time series prediction problem, in comparison with three benchmark statistical approaches. Overall, the experimental results demonstrate the superiority of the BN-based models in meteorological prediction. The presently proposed spatial fuzzy Bayesian network (SpaFBN) is also found to be an effective tool, especially for predicting humidity and precipitation rate time series. Moreover, the proposed SpaFBN is a generic CI technique which can be applied for predicting spatial time series from the domains other than meteorology as well.