Person:
Troncoso, Alicia

Catedrático/a de Universidad
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First Name
Alicia
Last Name
Troncoso
Affiliation
Universidad Pablo de Olavide
Department
Deporte e Informática
Research Center
Area
Lenguajes y Sistemas Informáticos
Research Group
Data Science & Big Data Lab
PAIDI Areas
Tecnologías de la Información y la Comunicación
PhD programs
Control de Gestión y Finanzas, Ingeniería, Ciencia de Datos y Bioinformática
Identifiers
UPO investigaORCIDScopus Author IDWeb of Science ResearcherIDDialnet IDGoogle Scholar ID

Search Results

Now showing 1 - 7 of 7
  • Publication
    A deep LSTM network for the Spanish electricity consumption forecasting
    (Springer, 2022-02-05) Torres, José. F.; Martínez-Álvarez, F.; Troncoso, Alicia
    Nowadays, electricity is a basic commodity necessary for the well-being of any modern society. Due to the growth in electricity consumption in recent years, mainly in large cities, electricity forecasting is key to the management of an efficient, sustainable and safe smart grid for the consumer. In this work, a deep neural network is proposed to address the electricity consumption forecasting in the short-term, namely, a long short-term memory (LSTM) network due to its ability to deal with sequential data such as time-series data. First, the optimal values for certain hyper-parameters have been obtained by a random search and a metaheuristic, called coronavirus optimization algorithm (CVOA), based on the propagation of the SARS-Cov-2 virus. Then, the optimal LSTM has been applied to predict the electricity demand with 4-h forecast horizon. Results using Spanish electricity data during nine years and half measured with 10-min frequency are presented and discussed. Finally, the performance of the proposed LSTM using random search and the LSTM using CVOA is compared, on the one hand, with that of recently published deep neural networks (such as a deep feed-forward neural network optimized with a grid search) and temporal fusion transformers optimized with a sampling algorithm, and, on the other hand, with traditional machine learning techniques, such as a linear regression, decision trees and tree-based ensemble techniques (gradient-boosted trees and random forest), achieving the smallest prediction error below 1.5%.
  • Publication
    Diseño y Aplicación de una Acción Tutorial para Asignaturas de Programación en la Escuela Politécnica Superior
    (2012) Giráldez, Raúl; Troncoso, Alicia; Aguilar-Ruiz, Jesús S.
    Según el Real Decreto 1791/2010, de 30 de diciembre, por el que se aprueba el Estatuto del Estudiante Universitario las universidades dentro del Espacio Europeo de Educación Superior deben impulsar los sistemas tutoriales que integren de forma coordinada acciones de información, orientación y apoyo a los estudiantes tanto en el seguimiento de su aprendizaje como en su adaptación al mundo universitario y en su transición al mundo laboral. En este trabajo se presenta un Plan de Acción Tutorial llevado a cabo en asignaturas de la titulación Ingeniería Técnica en Informática de Gestión de la Escuela Politécnica Superior con el objetivo de hacer partícipe al estudiante en su propio proceso de aprendizaje en lo que respecta a la adquisición de competencias.
  • Publication
    FS-Studio: An extensive and efficient feature selection experimentation tool for Weka Explorer
    (Elsevier, 2023) Chacón Maldonado, Andrés Manuel; Asencio Cortes, Gualberto; Martínez-Álvarez, Francisco; Troncoso, Alicia
    A new tool with a friendly graphical user interface specifically designed to perform feature selection experiments in Weka Explorer allowing parallel computation is proposed in this work. The proposed tool performs Bayesian statistical tests among the selected feature selection techniques to check whether the differences are statistically significant or not. Moreover, the recently published general- purpose metaheuristic named Coronavirus Optimization Algorithm is also adapted for feature selection and integrated in the proposed tool to search for attribute subsets, allowing its use along with any Weka attribute subset evaluation algorithm. After the feature selection process is performed, both classification and regression techniques can be applied to the dataset built with the most relevant features. Finally, the output of the whole process is sent to an exportable table, customizable by means of a bar plot, in order to gather both predicted and actual values as well as the evaluation metrics.
  • Publication
    Applying wrapper-based variable selection techniques to predict MFIs profitability: evidence from Peru
    (Routledge Taylor&Francis, 2021-02-15) Fabio Pietrapiana; Feria Domínguez, José Manuel; Troncoso, Alicia
    In this paper, we analyse the main factors explaining the profitability (ROA) of Microfinance Institutions (MFIs) in Peru from 2011 to 2107. We apply three wrapper techniques to asample of 168 Peruvians MFIs and 69 attributes obtained from MIX Market database. After running the algo-rithms M5ʹ, knearest neighbours (KNN) and Random Forest, we find that the M5ʹ algorithm provides the best fit for predicting ROA. Particularly, the key variable of the regression tree is the percentage of expenses over assets and, depending on its value, it is followed by net income after taxes and before donations, or profit margins
  • Publication
    A new hybrid method for predicting univariate and multivariate time series based on pattern forecasting
    (Elsevier, 2021-12-18) Castán Lascorz, Miguel Ángel; Jiménez Herrera, Patricia; Troncoso, Alicia; Asencio Cortés, Gualberto
    Time series forecasting has become indispensable for multiple applications and industrial processes. Currently, a large number of algorithms have been developed to forecast time series, all of which are suitable depending on the characteristics and patterns to be inferred in each case. In this work, a new algorithm is proposed to predict both univariate and multivariate time series based on a combination of clustering, classification and forecasting techniques. The main goal of the proposed algorithm is first to group windows of time series values with similar patterns by applying a clustering process. Then, a specific forecasting model for each pattern is built and training is only conducted with the time windows corresponding to that pattern. The new algorithm has been designed using a flexible framework that allows the model to be generated using any combination of approaches within multiple machine learning techniques. To evaluate the model, several experiments are carried out using different configurations of the clustering, classification and forecasting methods that the model consists of. The results are analyzed and compared to classical prediction models, such as autoregressive, integrated, moving average and Holt-Winters models, to very recent forecasting methods, including deep, long short-term memory neural networks, and to well-known methods in the literature, such as k nearest neighbors, classification and regression trees, as well as random forest.
  • Publication
    Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption
    (Elsevier, 2024-01-22) Pérez Chacón, Rubén; Asencio Cortés, Gualberto; Martínez-Álvarez, Francisco; Troncoso, Alicia
    Several interrelated variables typically characterize real-world processes, and a time series cannot be predicted without considering the influence that other time series might have on the target time series. This work proposes a novel algorithm to forecast multivariate big data time series. This new general-purpose approach consists first of a previous pattern recognition performed jointly using all time series that form the multivariate time series and then predicts the target time series by searching for similarities between pattern sequences. The proposed algorithm is designed to tackle multivariate time series forecasting problems within the context of big data. In particular, the algorithm has been developed with a distributed nature to enhance its efficiency in analyzing and processing large volumes of data. Moreover, the algorithm is straightforward to use, with only two parameters needing adjustment. Another advantage of the MV-bigPSF algorithm is its ability to perform multi-step forecasting, which is particularly useful in many practical applications. To evaluate the algorithm’s performance, real-world data from Uruguay’s power consumption has been utilized. Specifically, MV-bigPSF has been compared with both univariate and multivariate methods. Regarding the univariate ones, MV-bigPSF improved 12.8% in MAPE compared to the second-best method. Regarding the multivariate comparison, MV-bigPSF improved 44.8% in MAPE with respect to the second most accurate method. Regarding efficiency, the execution time of MV-bigPSF was 1.83 times faster than the second-fastest multivariate method, both in a single-core environment. Therefore, the proposed algorithm can be a valuable tool for practitioners and researchers working in multivariate time series forecasting, particularly in big data applications.
  • Publication
    Big data time series forecasting based on pattern sequence similarity and its application to the electricity demand
    (Elsevier, 2020-07-07) Pérez Chacón, Rubén; Asencio Cortés, Gualberto; Martínez Álvarez, Francisco; Troncoso, Alicia
    This work proposes a novel algorithm to forecast big data time series. Based on the well-established Pattern Sequence-based Forecasting algorithm, this new approach has two major contributions to the literature. First, the improvement of the original algorithm with respect to the accuracy of predictions, and second, its transformation into the big data context, having reached meaningful results in terms of scalability. The algorithm uses the Apache Spark distributed computation framework and it is a ready-to-use application with few parameters to adjust. Physical and cloud clusters have been used to carry out the experimentation, which consisted in applying the algorithm to real-world data from Uruguay electricity demand.