Linearity Testing Against a Fuzzy Rule-based Model

In this paper, we introduce a linearity test for fuzzy rule-based models in the framework of time series modeling. To do so, we explore a family of statistical models, the regime switching autoregressive models, and the relations that link them to the fuzzy rule-based models. From these relations, we derive a Lagrange Multiplier linearity test and some properties of the maximum likelihood estimator needed for it. Finally, an empirical study of the goodness of the test is presented.fuzzy rule-based models, time series, linearity test, statistical inference

Testing for Heteroskedasticity of the Residuals in Fuzzy Rule-Based Models

In this paper, we propose a new diagnostic checking tool for fuzzy rule-based modelling of time series. Through the study of the residuals in the Lagrange Multiplier testing framework we devise a hypothesis test which allows us to determine if the residual time series is homoscedastic or not, that is, if it has the same variance throughout time. This is another important step towards a statistically sound modelling strategy for fuzzy rule-based models.Spanish Ministerio de Ciencia e Innovaci´on (MICINN) under Project grants MICINN TIN2009- 14575 and CIT-460000-2009-4

Photovoltaic Forecasting: A state of the art

International audiencePhotovoltaic (PV) energy, together with other renewable energy sources, has been undergoing a rapid development in recent years. Integration of intermittent energy sources as PV or wind power is challenging in terms of power system management in large scale systems as well as in small grids. Indeed, PV energy is a variable resource that is difficult to predict due to meteorological uncertainty. To facilitate the penetration of PV energy, forecasting methods and techniques have been used. Being able to predict the future behavior of a PV plant is very important in order to schedule and manage the alternative supplies and the reserves. In this paper we presented an overview aiming at a classification attending to the different techniques of forecasting methods used for PV or solar prediction. Finally, recent new approaches that take into account the uncertainty of the estimation are introduced. First results of these kind of models are presented

Predictions of three mathematical models related with the COVID-19 Vaccination Strategy in Spain

El Ministerio de Sanidad ha coordinado tres estudios que han estimado el impacto de la Estrategia de Vacunación frente a COVID-19 en España. El objetivo era que los modelos ayudaran a establecer los grupos de población prioritarios para la vacunación, en un contex-to inicial de limitación de dosis. A partir de la misma información epidemiológica y de va-cunas se han elaborado tres modelos matemá-ticos distintos cuyos resultados apuntan en la misma dirección: combinada con el distancia-miento físico, la vacunación escalonada, em-pezando por los grupos de mayor riesgo de complicaciones, evitaría el 60% de las infec-ciones, el 42% de las hospitalizaciones y el 60% de la mortalidad en la población. Estos modelos, que pueden adaptarse a la nueva evi-dencia científica disponible, son herramientas dinámicas y potentes para la evaluación y el ajuste de los programas de vacunación, impul-sando el desarrollo de este campo de inves-tigación, y ayudando a lograr resultados más eficientes en salud

Modelado de series temporales mediante sistemas basados en reglas difusas: un enfoque estadístico

Contiene un amplio resumen de la tesis escrito en españolTesis Univ. Granada. Departamento de Ciencias de la Computación e Inteligencia Artificial. Leída el 17 de octubre de 200

Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks

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

systems: Functional equivalence and consequences

transition autoregressive models and fuzzy rule-base

Testing for remaining autocorrelation of the residuals in the framework of fuzzy rule-based time series modeling

International audienceIn 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

Linearity Testing Against a Fuzzy Rule-based Model

In this paper, we introduce a linearity test for fuzzy rule-based models in the framework of time series modeling. To do so, we explore a family of statistical models, the regime switching autoregressive models, and the relations that link them to the fuzzy rule-based models. From these relations, we derive a Lagrange Multiplier linearity test and some properties of the maximum likelihood estimator needed for it. Finally, an empirical study of the goodness of the test is presented

Empirical study of feature selection methods based on individual feature evaluation for classification problems

International audienceThe use of feature selection can improve accuracy, efficiency, applicability and understandability of a learning process and its resulting model. For this reason, many methods of automatic feature selection have been developed. By using a modularization of feature selection process, this paper evaluates a wide spectrum of these methods. The methods considered are created by combination of different selection criteria and individual feature evaluation modules. These methods are commonly used because of their low running time. After carrying out a thorough empirical study the most interesting methods are identified and some recommendations about which feature selection method should be used under different conditions are provide