Search

Abstract This paper suggests a fuzzy inference system (iFIS) modeling approach for interval-valued time series forecasting. Interval-valued data arise quite naturally in many situations in which such data represent uncertainty/variability or when comprehensive ways to summarize large data sets are required. The method comprises a fuzzy rule-based framework with affine consequents which provides a (non)linear framework that processes interval-valued symbolic data. The iFIS antecedents identification uses a fuzzy c-means clustering algorithm for interval-valued data with adaptive distances, whereas parameters of the linear consequents are estimated with a center-range methodology to fit a linear regression model to symbolic interval data. iFIS forecasting power, measured by accuracy metrics and statistical tests, was evaluated through Monte Carlo experiments using both synthetic interval-valued time series with linear and chaotic dynamics, and real financial interval-valued time series. The results indicate a superior performance of iFIS compared to traditional alternative single-valued and interval-valued forecasting models by reducing 19% on average the predicting errors, indicating that the suggested approach can be considered as a promising tool for interval time series forecasting. [ABSTRACT FROM AUTHOR]

China is often referred to as a global benchmark for the energy transition. Regarding energy sustainability, China's strategies and policies might be viewed as either highly innovative or detrimental. Considering the Chinese economy, population, consumption, and pollution rates, the country's energy transition decisions are deemed major determinants for the present and future global energy sustainability, resilience and related conditions. This paper aims to provide a measure and definition of these phenomena to characterize selected aspects, highlighting China's global and regional status. To this end, an interval-based composite indicator assessment is reviewed, indicating that China holds a mid-tier status in terms of energy sustainability relative to global standards. Therefore, energy security standards serve as the foundation for China's transition path to ensuring a clean, renewable and sustainable energy supply and resilience in the future conditional to universal and democratic energy justice achievement. • China's energy transition, resilience and sustainable development are analyzed. • An interval-data composite indicator is constructed. • The global and regional status of China in energy change is measured. • Energy security standards serve as the foundation for China's transition path. [ABSTRACT FROM AUTHOR]

Entropy quantification algorithms are becoming a prominent tool for the physiological monitoring of individuals through the effective measurement of irregularity in biological signals. However, to ensure their effective adaptation in monitoring applications, the performance of these algorithms needs to be robust when analysing time-series containing missing and outlier samples, which are common occurrence in physiological monitoring setups such as wearable devices and intensive care units. This paper focuses on augmenting Dispersion Entropy (DisEn) by introducing novel variations of the algorithm for improved performance in such applications. The original algorithm and its variations are tested under different experimental setups that are replicated across heart rate interval, electroencephalogram, and respiratory impedance time-series. Our results indicate that the algorithmic variations of DisEn achieve considerable improvements in performance while our analysis signifies that, in consensus with previous research, outlier samples can have a major impact in the performance of entropy quantification algorithms. Consequently, the presented variations can aid the implementation of DisEn to physiological monitoring applications through the mitigation of the disruptive effect of missing and outlier samples.

The paper proposes an extension for well-known COBWEB algorithm for different symbolic variable types. This extension allows to apply symbolic variables for category utility evaluation. Main body of the paper presents how to adapt category utility for different symbolic variable types. Example shows an illustrative example of the proposed method.

Abstract The Principal Component Analysis (PCA) is one of the most known and used linear statistical methods for process monitoring. However, the PCA algorithm is not designed to handle the uncertainty of the sensor measurements that is represented by an interval type data. Including uncertainty of the sensors measurements in the analysis requires extending the PCA methodology to the Symbolic Data Analysis (SDA). The SDA refers to a paradigm where statistical units are described by interval-valued variables. In this regard, Symbolic Principal Component Analysis (SPCA), particularly Midpoints-Radii PCA (MRPCA) technique, is investigated for modeling and diagnosis of uncertain data. The aim of the present paper is to propose an extended version of the linear SPCA technique, based on midpoints and radii, to the nonlinear case of kernel PCA method (MR-KPCA). The basic idea is to construct a robust KPCA model from midpoints and radii of the nonlinear uncertain process data. Then, the robust KPCA model is used for diagnosis (FDI) purpose. In fact, the FDI decisions are improved by taking in to account the uncertainties on the nonlinear data. The MR-KPCA algorithm is applied for sensor fault detection and isolation of an automatic weather station. The results of applying this algorithm show its feasibility and advantageous performances. Highlights • Nonlinear modeling technique designed to handle uncertain nonlinear systems. • Robust KPCA modeling methodology can handle the nonlinear interval type data. • Improved FDI decisions by considering the uncertainties in the nonlinear data. • Nonlinear fault isolation method to identify the roots of the uncertain faults. [ABSTRACT FROM AUTHOR]

Highlights • This study aims to propose interval quantile regression models based on swarm intelligence. • Proposed models are compared with methods of estimation based on linear programming widely used in the literature of Quantile Regression. • Experiments with real-life and simulated data sets are considered. • The robustness and the precision of these models are shown. Abstract This paper presents quantile regression models for classical data and interval symbolic data using algorithms based on swarm intelligence to estimate the parameters aiming to improve the model performance. Also, these methods are compared with methods of estimation based on linear programming widely used in the literature. Applications using real and simulated data are considered. The prediction quality is assessed by the mean magnitude of relative error calculated from test data. The growth of symbolic data nature of alerts to the need of the new statistical methodologies development for the treatment of this type of information. The results show that the proposed models are effective alternatives for optimizing the choice of parameters of the quantile regression, providing greater precision and robustness than models based on linear programming. [ABSTRACT FROM AUTHOR]

This work presents an approach based on the kernelized discriminant analysis to classify symbolic interval data in nonlinearly separable problems. It is known that the use of kernels allows to map implicitly data into a high-dimensional space, called feature space; computing projections in this feature space results in a nonlinear separation in the input space that is equivalent to linear separating function in the feature space. In this work, the kernel matrix is obtained based on kernelized interval inner product. Experiments with synthetic interval data sets and an application with a Brazilian thermographic breast database demonstrate the usefulness of this approach. [ABSTRACT FROM AUTHOR]

It is well-known that the values of symbolic variables may take various forms such as an interval, a set of stochastic measurements of some underlying patterns or qualitative multi-values and so on. However, the majority of existing work in symbolic data analysis still focuses on interval values. Although some pioneering work in stochastic pattern based symbolic data and mixture of symbolic variables has been explored, it still lacks flexibility and computation efficiency to make full use of the distinctive individual symbolic variables. Therefore, we bring forward a novel hierarchical clustering method with weighted general Jaccard distance and effective global pruning strategy for complex symbolic data and apply it to emitter identification. Extensive experiments indicate that our method has outperformed its peers in both computational efficiency and emitter identification accuracy. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) program has been widely applied for damage assessment or monitoring the performances and the reliabilities of structures. Usually, structural damage is assessed by discriminating abnormal change from normal change in the structural dynamic behavior. This paper takes the SHM data from a real bridge as a study example and recognizes the trains which daily cross this bridge. The main aim of the current work is to improve the accuracy of structural damage detection. In this paper, symbolic data analysis (SDA) is introduced to extract hidden patterns from raw data, and principal component analysis (PCA) is performed to extract the most important properties of the signals and eliminate the influence of noise. A new representative is proposed in this paper and the recognition results obtained by the clustering algorithms with different representatives are compared. The results show that the new representative is robust to recognize the trains, and PCA is very useful to extract the properties of the signals as well as to reduce the influence of noise. [ABSTRACT FROM AUTHOR]

Copyright of Research Papers of the Wroclaw University of Economics / Prace Naukowe Uniwersytetu Ekonomicznego we Wroclawiu is the property of Uniwersytet Ekonomiczny we Wroclawiu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

El método STATIS, propuesto por L’Hermier des Plantes y Escoufier, se utiliza para analizar múltiples tablas de datos en las cuales es muy fre- cuente que cada una de la tablas tenga información referente al mismo conjunto de individuos. Las diferencias y similitudes entre dichas tablas se analizan por medio de una estructura llamada compromiso. En este trabajo se presenta un nuevo algoritmo, denominado INTERSTATIS, del método STATIS para el caso cuando los datos de entrada son todos de tipo inter- valo. Esta propuesta se basa en la aritmética de intervalos de Moore y el Método de Centros para el Análisis de Componentes Principales con datos de tipo intervalo, propuesto por Cazes et al. [5]. Además de presentar el método INTERSTATIS de forma algorítmica, un ejemplo de ejecución es presentado, junto con la interpretación de sus resultados.

The center method, which was first proposed in Rev. Stat. Appl. 1997 by Cazes et al. and Stat. Anal. Data Mining 2011 by Douzal-Chouakria et al., extends the well-known Principal Component Analysis (PCA) method to particular types of symbolic objects that are characterized by multivalued interval-type variables. In contrast to classical data, symbolic data have internal variation. The authors who originally proposed the center method used the center of a hyper-rectangle in R m as a base point to carry out PCA, followed by the projection of all vertices of the hyper-rectangles as supplementary elements. Since these publications, the center point of the hyper-rectangle has typically been assumed to be the best point for the initial PCA. However, in this paper, we show that this is not always the case, if the aim is to maximize the variance of projections or minimize the squared distance between the vertices and their respective projections. Instead, we propose the use of an optimization algorithm that maximizes the variance of the projections (or that minimizes the distances between the squares of the vertices and their respective projections) and finds the optimal point for the initial PCA. The vertices of the hyper-rectangles are, then, projected as supplementary variables to this optimal point, which we call the “Best Point” for projection. For this purpose, we propose four new algorithms and two new theorems. The proposed methods and algorithms are illustrated using a data set comprised of measurements of facial characteristics from a study on facial recognition patterns for use in surveillance. The performance of our approach is compared with that of another procedure in the literature, and the results show that our symbolic analyses provide more accurate information. Our approach can be regarded as an optimization method, as it maximizes the explained variance or minimizes the squared distance between projections and the original points. In addition, the symbolic analyses generate more informative conclusions, compared with the classical analysis in which classical surrogates replace intervals. All the methods proposed in this paper can be executed in the RSDA package developed in R.

Interval-valued data arises in situations where it is needed to manage either the uncertainty related to measurements, or the variability inherent to a group rather than an individual. This paper proposes a fuzzy c-means clustering algorithm based on suitable adaptive City-Block and Hausdorff distances with the purpose to cluster interval-valued data. This fuzzy c-means clustering algorithm optimizes explicitly an objective function by alternating three steps aiming to compute the fuzzy cluster representatives, the fuzzy partition, as well as relevance weights for the interval-valued variables. Indeed, most often conventional fuzzy c-means clustering algorithms consider that all variables are equally important for the clustering task. However, in real situations, some variables may be more or less important or even irrelevant for clustering. Due to the use of adaptive City-Block and Hausdorff distances, the paper proposes a fuzzy c-means clustering algorithm that tackles this problem with a step where a relevance weight is automatically learned for each interval-valued variable. Additionally, various tools for the fuzzy partition and fuzzy cluster interpretation of interval-valued data provided by this algorithm is also presented. Experiments with suitable synthetic and real interval-valued datasets demonstrate the robustness and the usefulness of this fuzzy c-means clustering algorithm and the merit of the fuzzy partition and fuzzy cluster interpretation tools. [ABSTRACT FROM AUTHOR]

Interval symbolic data is a complex data type that can often be obtained by summarizing large datasets. All existing linear regression approaches for interval data use certain fixed reference points to model intervals, such as midpoints, ranges and lower and upper bounds. This is a limitation, because different datasets might be better represented by different reference points. In this paper, we propose a new method for extracting knowledge from interval data. Our parametrized approach automatically extracts the best reference points from the regressor variables. These reference points are then used to build two linear regressions: one for the lower bounds of the response variable and another for its upper bounds. Before the regressions are applied, we compute a criterion to verify the mathematical coherence of predicted values. Mathematical coherence means that the upper bounds are greater than the lower bounds. If the criterion shows that the coherence is not guaranteed, we suggest the use of a novel interval Box-Cox transformation of the response variable. Experimental evaluations with synthetic and real interval datasets illustrate the advantages and the usefulness of the proposed method to perform interval linear regression. [ABSTRACT FROM AUTHOR]

Demographic processes across European regions show great diversity, but because of this diversity, it is hard to gain an overview of similarities and differences. This paper aims to examine the application of a new combination of existing approaches to the analysis of regional population pyramids to offer such an overview. Symbolic data analysis and cluster analysis are used to identify typical shapes of population pyramids, before applying join-count tests to examine the spatial distribution of these pyramid shapes. The data used are for 1397 NUTS regional units in 37 European countries in 2015. We find that Irish regions, Cyprus and some of the capital cities of Western Europe present the youngest population across Europe, while the population of Eastern Germany is the oldest and shrinking in size. Countries of East-central Europe are the most homogeneous in their demographic processes for the chosen period, while the large demographic discrepancies occur within Spain, France, the UK, Finland, and Sweden between NUTS regions. A spatial study indicated positive spatial autocorrelation, and the transnational character of demographic processes across Europe. For a detailed examination of East-central Europe, we applied local statistics, and revealed three transnational spatial clusters resulting from historical and socio-economic processes. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the leading causes of death in women. Because of this, thermographic images have received a refocus for diagnosing this cancer type. This work proposes an innovative approach to classify breast abnormalities (malignant, benignant and cyst), employing interval temperature data in order to detect breast cancer. The learning step takes into account the internal variation of the intervals when describing breast abnormalities and uses a way to map these intervals into a space where they can be more easily separated. The method builds class prototypes, and the allocation step is based on a parameterized Mahalanobis distance for interval-valued data. The proposed classifier is applied to a breast thermography dataset from Brazil with 50 patients. We investigate two different scenarios for parameter configuration. The first scenario focuses on the overall misclassification rate and achieves 16 % misclassification rate and 93 % sensitivity to the malignant class. The second scenario maximizes the sensitivity to the malignant class, achieving 100 % sensitivity to this specific class, along with 20 % overall misclassification rate. We compare the performances of our approach and of many methods taken from the literature of interval data classification for the breast thermography task. Results show that our method outperforms competing algorithms. [ABSTRACT FROM AUTHOR]

Symbolic data analysis deals with complex data with symbolic objects, such as lists, histograms, and intervals. Spatial analysis for symbolic data is relatively underexplored. To fill the gap, this paper proposes a statistical framework for spatial interval-valued data (SIVD) analysis. We provide geostatistical methods for spatial prediction, predictive performance measure for prediction assessment, and visualization for mapping SIVD. The proposed methods are illustrated with both simulated and real examples., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2023 Informa UK Limited, trading as Taylor & Francis Group.)