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In this paper, the problem of predicting future concentrations of airborne pollen is solved through a computational intelligence data-driven approach. The proposed method is able to identify the most important variables among those considered by other authors (mainly recent pollen concentrations and weather parameters), without any prior assumptions about the phenological relevance of the variables. Furthermore, an inferential procedure based on non-parametric hypothesis testing is presented to provide statistical evidence of the results, which are coherent to the literature and outperform previous proposals in terms of accuracy. The study is built upon Poaceae airborne pollen concentrations recorded in seven different locations across the Spanish province of Madrid. [ABSTRACT FROM AUTHOR]

Soft computing (SC) emerged as an integrating framework for a number of techniques that could complement one another quite well (artificial neural networks, fuzzy systems, evolutionary algorithms, probabilistic reasoning). Since its inception, a distinctive goal has been to dig out the deep relationships among their components. This paper considers two wide families of SC models. On the one hand, the regime-switching autoregressive paradigm is a recent development in statistical time series modeling, and it includes a set of models closely related to artificial neural networks. On the other hand, we consider fuzzy rule-based systems in the framework of time series analysis. This paper discloses original results establishing functional equivalences between models of these two classes, and hence opens the door to a productive line of research where results and techniques from one area can be applied in the other. As a consequence of the equivalences presented in this paper, we prove the asymptotic stationarity of a class of fuzzy rule-based systems. Simulations based on information criteria show the importance of the selection of the proper membership function. [ABSTRACT FROM AUTHOR]

In time series analysis remaining autocorrelation in the errors of a model implies that it is failing to properly capture the structure of time-dependence of the series under study. This can be used as a diagnostic checking tool and as an indicator of the adequacy of the model. Through the study of the errors of the model in the Lagrange Multiplier testing framework, in this paper we derive (and validate using simulated and real world examples) a hypothesis test which allows us to determine if there is some left autocorrelation in the error series. This represents a new diagnostic checking tool for fuzzy rule-based modelling of time series and is an important step towards statistically sound modelling strategy for fuzzy rule-based models. [ABSTRACT FROM AUTHOR]

Airborne pollen monitoring datasets sometimes exhibit gaps, even very long, either because of maintenance or because of a lack of expert personnel. Despite the numerous imputation techniques available, not all of them effectively include the spatial relations of the data since the assumption of missing-at-random is made. However, there are several techniques in geostatistics that overcome this limitation such as the inverse distance weighting and Gaussian processes or kriging. In this paper, a new method is proposed that utilizes convolutional neural networks. This method not only shows a competitive advantage in terms of accuracy when compared to the aforementioned techniques by improving the error by 5% on average, but also reduces execution training times by 90% when compared to a Gaussian process. To show the advantages of the proposal, 10%, 20%, and 30% of the data points are removed in the time series of a Poaceae pollen observation station in the region of Madrid, and the airborne concentrations from the remaining available stations in the network are used to impute the data removed. Even though the improvements in terms of accuracy are not significantly large, even if consistent, the gain in computational time and the flexibility of the proposed convolutional neural network allow field experts to adapt and extend the solution, for instance including meteorological variables, with the potential decrease of the errors reported in this paper. [ABSTRACT FROM AUTHOR]

In this brief, we present a novel model fitting procedure for the neuro-coefficient smooth transition autoregressive model (NCSTAR), as presented by Medeiros and Veiga. The model is endowed with a statistically founded iterative building procedure and can be interpreted in terms of fuzzy rule-based systems. The interpretability of the generated models and a mathematically sound building procedure are two very important properties of forecasting models. The model fitting procedure employed by the original NCSTAR is a combination of initial parameter estimation by a grid search procedure with a traditional local search algorithm. We propose a different fitting procedure, using a memetic algorithm, in order to obtain more accurate models. An empirical evaluation of the method is performed, applying it to various real-world time series originating from three forecasting competitions. The results indicate that we can significantly enhance the accuracy of the models, making them competitive to models commonly used in the field.