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The need to consider data that contain information that cannot be represented by classical models has led to the development of symbolic data analysis (SDA). As a particular case of symbolic data, symbolic interval time series are interval-valued data which are collected in a chronological sequence through time. This paper presents two approaches to symbolic interval time series analysis. The first...
This paper reports the application of artificial neural networks for estimating reference evapotranspiration (ETo) as a function of local maximum and minimum air temperatures and exogenous relative humidity and evapotranspiration in twelve coastal locations of the autonomous Valencia region, Spain. The Penman-Monteith model for ETo prediction, as been proposed by the Food and Agriculture Organization...
This paper proposes a new adaptive method for prediction, i.e., prediction by logic reasoning. The paper presents the differences between the traditional prediction methods based on analytic simulation and the new prediction method based on logic reasoning, discusses the logical basis of the new prediction method, and shows that the family of temporal relevant logics can be adopted as the fundamental...
Many problems involve not structured environments which can be solved from the perspective of particle swarm optimization (PSO). In this research analyze the voting behavior in a popular song contest held every year in Europe. The dataset makes it possible to analyze the determinants of success, and gives a rare opportunity to run a direct test of vote trading from logrolling. We show that they are...
The integration of feature selection techniques within the modeling process of a time series forecaster can improve dealing with some usual important problems in this type of tasks, such as noise reduction, the curse of dimensionality and reducing the complexity of both the problem and the solution. In this paper we show how a convenient combination of feature selection procedures with soft computing...
This paper presents a Brazilian case study of forecasting a wind speed time series with reservoir computing (RC). RC is a research area, in which an untrained recurrent network of nodes is used for the recognition of temporal patters. In RC only the weights of the connections in a linear output layer are trained. This reduces the complexity of recurrent neural networks (RNN) training to simple linear...
This paper presents the hybridization of global and mesoscale weather forecasting models with neural networks in order to tackle a problem of short-term wind speed prediction. The mean hourly wind speed forecast at aero-generators in a wind park is an important parameter used to predict the total energy production of the park. Our model for short-term wind speed forecast integrates two different meteorological...
This paper presents a novel interval type-2 fuzzy inference system with automatic learning for handling uncertainty, called the hierarchical type-2 neuro-fuzzy BSP model (T2-HNFB). This new model combines the paradigms of the type-2 fuzzy inference systems and neural networks with recursive partitioning techniques (BSP - Binary Space Partitioning). The model is able to automatically create and expand...
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