Testing for heteroskedasticity of the residuals in fuzzy rule-based models

International audienceIn 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

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

Intelligent system for non-technical losses management in residential users of the electricity sector

The identification of irregular users is an important assignment in the recovery of energy in the distribution sector. This analysis requires low error levels to minimize non-technical electrical losses in power grid. However, the detection of fraudulent users who have billing does not present a generalized methodology. This issue is complex and varies according to the case study. This paper presents a novel methodology to identify residential fraudulent users by using intelligent systems. The proposed intelligent system consists of three fundamental modules. The first module performs the classification of users with similar power consumption curves using self-organizing maps and genetic algorithms. The second module allows carrying out the monthly electricity demand forecasting through of recursive adjustment of ARIMA models. The third module performs the detection of fraudulent users through an artificial neural network for pattern recognition. For the design and validation of the proposed intelligent system, several tests were performed in each developed module. The database used for the design and evaluation of the modules was constructed with data supplied by the energy distribution company of the Colombian Caribbean Region. The results obtained by the proposed intelligent system show a better performance versus the detection rates obtained by the company.La identificación de usuarios con consumo fraudulento es una actividad importante en la recuperación de energía en el sector de la distribución. Este análisis requiere bajos niveles de error para minimizar las pérdidas eléctricas no técnicas en la red de distribución. Sin embargo, la detección de usuarios fraudulentos con facturación no tiene una metodología generalizada. Este es un problema complejo y varía de acuerdo con cada caso de estudio. Este artículo presenta una nueva metodología para la identificación inteligente de usuarios fraudulentos residenciales basada en sistemas inteligentes. El sistema inteligente propuesto consiste en tres módulos fundamentales. El primer módulo clasifica a los usuarios con curvas de consumo similares a través de mapas auto-organizativos y algoritmo genéticos. El segundo módulo realiza la predicción de consumos mensuales mediante ajustes recursivos de modelos ARIMA. El tercer módulo es el responsable de llevar a cabo la detección de usuarios irregulares por medio de una red neuronal para reconocimiento de patrones. Para el diseño y validación del sistema inteligente propuesto se realizaron pruebas en cada módulo que lo integra para diferentes tipos de clientes del mercado. La base de datos utilizada para el diseño y evaluación de los módulos fue construida a partir de los datos suministrados por la empresa de distribución de energía de la Costa Caribe Colombiana. Los resultados obtenidos por el sistema inteligente propuesto muestran un mejor desempeño frente a los índices de detección obtenidos por la empresa

Energy forecasting in smart grid systems: recent advancements in probabilistic deep learning

Energy forecasting plays a vital role in mitigating challenges in data rich smart grid (SG) systems involving various applications such as demand-side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty in SG data. This paper presents a comprehensive and application-oriented review of state-of-the-art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL). Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data pre-processing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption in Australia and American electric power (AEP) datasets is conducted to analyze the performance of deterministic and probabilistic forecasting methods. The analysis demonstrates higher efficacy of DL methods with appropriate hyper-parameter tuning when sample sizes are larger and involve nonlinear patterns. Furthermore, PDL methods are found to achieve at least 60% lower prediction errors in comparison to other benchmark DL methods. However, the execution time increases significantly for PDL methods due to large sample space and a tradeoff between computational performance and forecasting accuracy needs to be maintained

Don't blame the model:Reconsidering the network approach to psychopathology

The network approach to psychopathology is becoming increasingly popular. The motivation for this approach is to provide a replacement for the problematic common cause perspective and the associated latent variable model, where symptoms are taken to be mere effects of a common cause (the disorder itself). The idea is that the latent variable model is plausible for medical diseases, but unrealistic for mental disorders, which should rather be conceptualized as networks of directly interacting symptoms. We argue that this rationale for the network approach is misguided. Latent variable (or common cause) models are not inherently problematic, and there is not even a clear boundary where network models end and latent variable (or common cause) models begin. We also argue that focusing on this contrast has led to an unrealistic view of testing and finding support for the network approach, as well as an oversimplified picture of the relationship between medical diseases and mental disorders. As an alternative, we point out more essential contrasts, such as the contrast between dynamic and static modeling approaches that can provide a better framework for conceptualizing mental disorders. Finally, we discuss several topics and open problems that need to be addressed in order to make the network approach more concrete and to move the field of psychological network research forward. (PsycINFO Database Recor

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

Impact of Temporal Order Selection on Clustering Intensive Longitudinal Data Based on VAR Models

In real-world research, intensive longitudinal data (ILDs) are typically collected from a group of individuals of interest, which enables researchers to model not only the within-individual dynamics of the studied processes but also the between-individual differences on the within-individual dynamics. Among the statistical techniques proposed for modeling ILDs of multiple individuals, clustering of intensive longitudinal data provides a meaningful way to quantify sample heterogeneity in dynamic processes, assuming that such heterogeneity reflects the distinct nature of the studied processes. The aims of this dissertation are threefold: (a) to introduce a VAR-based clustering technique, (b) to examine the impact of temporal order selection on clustering accuracy and parameter estimation by a simulation study, and (c) to demonstrate the application of the clustering technique through an empirical analysis. Specially, I investigated the influence of two temporal order selection strategies: (1) using the most complex structure or highest order (HO) for all individual processes, and (2) using the most parsimonious structure or the lowest order (LO) for all individuals on the performance of two-step model-based clustering procedure. This procedure extracted dynamic coefficients from vector autoregressive (VAR) models and employed the Gaussian mixture model (GMM) and K-means clustering algorithms on the coefficients for cluster identification. Additionally, I also examined whether the influence varied across two clustering algorithms. The simulation study showed that, regardless of the clustering algorithms used, LO strategy consistently outperformed HO strategy in terms of recovering the number of clusters, cluster membership, and cluster-specific AR and CR effects. GMM performed better than K-means when LO strategy was applied; however, the performance of GMM decreased while the temporal orders increased. Additionally, GMM showed more vulnerability with smaller numbers of participants. The application of the two-step VAR-based method to affect data yielded a meaningful and informative clustering solution, which provided further insights of the uses of the model-based clustering approach Lastly, suggestions and recommendations were offered based on the results of the simulation and empirical analyses

NCG80/3: Máster erasmus+: Color in Science and Industry (COSI)

Máster ERASMUS +: Color in Science and Industry (COSI). Aprobado en la sesión ordinaria del Consejo de Gobierno de 8 de abril de 201