Abstract In this paper, we introduce a new general class of trigonometric distributions based on the tangent function, called the Tan-G class. A mathematical procedure of the Tan-G class is carried out, including expansions for the probability density function, moments, central moments and Rényi entropy. The estimates are acquired in a non-closed form by the maximum likelihood estimation method. Then, an emphasis is put on a particular member of this class defined with the Burr XII distribution as baseline, called the Tan-BXII distribution. The inferential properties of the Tan-BXII model are investigated. Finally, the Tan-BXII model is applied to a practical data set, illustrating the interest of the Tan-G class for the practitioner.

Resumen En este artículo, introducimos una nueva clase general de distribuciones trigonométricas basada en la función tangente, llamada la clase Tan-G. Se lleva a cabo un procedimiento matemático para la clase Tan-G, incluyendo expansiones para la función de densidad de probabilidad, momentos, momentos centrales y entropía de Rényi. Las estimaciones se obtienen en forma no-cerrada para el método de estimación de máxima verosimilitud. Luego, se pone énfasis en un miembro particular de esta clase definido con la distribución Burr XII como línea de base, llamada la distribución Tan-BXII. Se investigan las propiedades inferenciales del modelo Tan-BXII. Finalmente, el modelo Tan-BXII es aplicado para un conjunto de datos prácticos, ilustrando el interés de la clase Tan-G para el practicante.

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.