Maestría en Ciencias de los Datos y Analítica (tesis)
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Publicación Optimización de portafolios de inversión en el contexto de Big Data : integrando aprendizaje automático y técnicas de descomposición espectral(Universidad EAFIT, 2025) Hernández Slait, Jhon Jairo; Almonacid Hurtado, Paula MaríaPublicación Modelo AR-MIMO : mejora del pronóstico de múltiples horizontes en series de tiempo con optimizaciones heurísticas(Universidad EAFIT, 2025) Arias Zuluaga, Pablo Simón; Saldarriaga Aristizábal, Pablo AndrésPublicación Implementación de modelos de machine learning para la predicción de tendencias en pares de divisas del mercado forex(Universidad EAFIT, 2025) Ramírez Escobar, Sebastián; Almonacid Hurtado, Paula MaríaPublicación Reducción de ruido en señales bioacústicas : un enfoque basado en wavelets y aplicado al monitoreo de poblaciones de aves y anfibios(Universidad EAFIT, 2025) Carvalho Salazar, Sebastián; García Vargas, Johan FelipeBioacoustic monitoring techniques enable non-invasive detection of biological populations through automatic recorders that continuously capture species vocalizations in natural habitats. This study assesses the impact of wavelet-based noise reduction on bioacoustic signal processing and evaluates its influence on benchmark classification models, specifically BirdNET for birds and AnuraSet for amphibians. Our methodology includes noise reduction preprocessing, followed by an in-depth analysis of classification performance metrics such as mel cosine similarity, temporal correlation, entropy ratio, and ROC-AUC curves. Results indicate that noise reduction enhances signal clarity and reduces false alarm rates, enabling more accurate discrimination in acoustically complex environments like urban areas and rainforests. Although the technique may suppress some subtle vocalization features, statistical analysis and radar plots suggest that adjustments to the denoising process can help optimize the balance between noise reduction and preservation of essential bioacoustic characteristics. Consequently, wavelet-based noise reduction is a robust strategy for high-interference environments, though it may be less suitable for studies requiring comprehensive capture of all vocalizations, such as endangered or low-density species. Moreover, denoising regulates confidence in incorrect predictions and preserves relevant features in correct predictions, reducing false alarms.Ítem Identificación de la satisfacción de los empleados en programas de formación bancaria mediante grandes modelos de lenguaje natural(Universidad EAFIT, 2024) Ávila Arias, Juan Sebastián; Martínez Vargas, Juan DavidÍtem Modelo de recomendación de nuevos productos a clientes actuales(Universidad EAFIT, 2024) Isaza Higuera, Pablo; Sepúlveda Cano, Lina MaríaÍtem Modelación de excedencias de periodos secos y húmedos en la cuenca del río Porce mediante procesos de Poisson no homogéneos(Universidad EAFIT, 2024) Ferrucho Maloof, Isaac Eli; Suárez Sierra, Biviana Marccela; Carmona Duque, Alejandra MaríaThe present study analyzes the periods of precipitation deficits and excesses in the Porce river basin, Colombia, during the period 1970 to 2023. Using the Standardized Precipitation Index (SPI) and monthly precipitation series data from meteorological stations selected for their data completeness in the Porce river basin, a model based on Non-Homogeneous Poisson Processes (NHPP) was developed and applied to identify and characterize these periods. Different NHPP configurations, such as linear, potential and exponential intensity functions, were evaluated. The results indicate that power-law and linear models, in most cases, provide a superior fit for estimating drought and wet periods, while exponential models presented notable limitations in the ability to accurately represent extreme drought and wet events. This finding underscores the importance of choosing appropriate models that respond to the climatic and geographic particularities of the region, contributing significantly to the improvement of water resources management and planning.Ítem Currency Prediction : Stochastic hybrid diferencial equations with LSTM(Universidad EAFIT, 2024) Arbeláez Betancur, Hoover Arley; Marín Sánchez, Fredy HernánÍtem Clasificación de inventarios multicriterio mediante el uso de Modelos de Aprendizaje Automático (ML) en la industria automotriz(Universidad EAFIT, 2024) Vesga Vesga, Luis Rodrigo; Castro Zualuaga, Carlos AlbertoÍtem Modeling the Ripple Effects of Company-Specific News on Correlated Stock Prices Based on Dynamic Time Warping Clustering(Universidad EAFIT, 2024) Gallego Montoya, Juan David; Ortiz Arias, SantiagoÍtem Generación de direcciones pseudo-aleatorias a partir de proyecciones de extrema curtosis para el cálculo de atipicidad Stahel-Donoho(Universidad EAFIT, 2024) Orozco Ortiz, Karla Cristina; Ortiz Arias, SantiagoÍtem Automatic Electrical Meter Forecasting : a Benchmarking Between Quantum Machine Learning and Classical Machine learning(Universidad EAFIT, 2024) Montes Castro, Jonathan Javier; Lalinde Pulido, Juan Guillermo; Sosa-Sierra, DanielThis work benchmarks Quantum Long Short-Term Memory (QLSTM) against classical LSTM networks using electrical meter data (KWh) from EPM, a public utility company, clients. The results show that QLSTM models learn in half the epochs compared to LSTM, as measured by the MSE cost function, while maintaining strong performance with respect to bias (Mean Absolute Percentage Error, MAPE) and variance (R^2) metrics. QLSTM leverages variational quantum circuits (VQC) to replace traditional LSTM cell gates, demonstrating the potential of quantum-hybrid algorithms in forecasting tasks. This study highlights the efficiency and accuracy advantages of quantum machine learning applied to real-world data from EPM’s electrical metering services.Ítem Modelo de predicción funcional de demanda de postes de repuesto en redes de distribución de energía a partir de regresiones Kernel(Universidad EAFIT, 2024) Arango Cañas, Diana Lisette; Ortiz Arias, SantiagoÍtem Metodología analítica para la estrategia de precios en pymes : una aplicación en el sector automotriz(Universidad EAFIT, 2024) Álvarez Restrepo, Victoria; Saldarriaga Aristizábal, Pablo AndrésÍtem Predicción de deserción de clientes en el mercado de seguros de transporte de carga en Estados Unidos mediante técnicas de Machine Learning : un caso de estudio(Universidad EAFIT, 2024) Uribe Durango, Víctor Ricardo; Almonacid Hurtado, Paula MaríaÍtem Sistema de reconocimiento de placas colombianas por medio de redes convolucionales para acceso a áreas residenciales(Universidad EAFIT, 2024) Pinto Restrepo, Daniel Enrique; Olarte Hernández, TomásÍtem Portfolio Optimization Using Predictive Auxiliary Classifier Generative Adversarial Networks : Application to the Colombian stock market(Universidad EAFIT, 2024) Arango López, Federico; Castellanos Ríos, SantiagoÍtem Estudio de la relación entre los valores sociales y la aceptación de sobornos como conducta corrupta : un estudio con modelos SEM y datos de la encuesta mundial de valores(Universidad EAFIT, 2024) Gómez Convers, Giovanny Hernando; Castrillón-Orrego, Sergio A.; Almonacid Hurtado, Paula MaríaIn a global context of rapid social change, investigating the relationship between social values and corruption has become increasingly urgent and significant. Which behaviors are desirable? Which do we manifest in daily life? The World Values Survey (WVS) serves as a crucial data source for understanding social values in various contexts. However, how these values influence the acceptance of bribery, and thus corruption, has not been sufficiently explored. This study examines the underlying patterns in response clusters and systematically analyzes them using the holistic possibilities offered by the institutionalism theoretical framework. The objective is to identify the most significant causalities and influences in the relationship between social values and corruption. Through robust data analysis, imputation techniques, dimensionality reduction, clustering analysis, and SEM modeling, we identify the main factors impacting the acceptance of bribery. The results demonstrate that the three pillars of institutionalism provide a valuable approach to understanding corruption by simultaneously considering key variables and components. When internalized, social values facilitate the acceptance of bribery in certain contexts, highlighting the influence of the cognitive dimension. Although legal frameworks can enhance transparency, cultural environment and customs have a more determining influence on the acceptance of corrupt practices. These findings underscore the need to foster a strong ethical culture and implement educational programs that promote integrity and transparency to effectively mitigate corruption.Ítem Modelo predictivo de insolvencia empresarial para las pymes en Colombia(Universidad EAFIT, 2024) Castro Espitia, Camilo; Martínez Vargas, Juan David; Sepúlveda Cano, Lina MaríaBusiness insolvency has an economic impact on the affected company and, directly and indirectly, on its suppliers of goods and services. This ends up affecting the general population, who are the consumers of these goods and services. Therefore, it is important to analyze and study the possible factors that lead a company to enter a situation of economic insolvency. In accordance with the above, the objective of this work is to predict business insolvency in the Colombian context for SMEs. Likewise, use will be made of the financial data available from the Superintendency of Companies in the year 2021 and the companies that entered into a situation of economic insolvency in the year 2023. In this way, through financial indicators, it will be possible to construct the explanatory variables. of economic insolvency and achieve a predictive model of economic insolvency. This work is approached as follows: first, information is extracted from the Superintendency of Companies for SMEs in 2021 and companies that entered economic insolvency in 2023 to obtain our objective variable. Then, based on the data obtained, the financial ratios necessary for this study are calculated, using the financial statements of these companies corresponding to the year 2021. Next, the exploratory data analysis is carried out to obtain a broader knowledge of the set. of data and analyze correlation issues between variables, among other aspects. Finally, several machine learning models are designed to determine which one is most appropriate for the case studyÍtem Comparativa de modelos para el reconocimiento de estructuras de datos tabulares : un enfoque desde el aprendizaje profundo(Universidad EAFIT, 2024) Garzón Vargas, José Miguel; Martínez Vargas, Juan David; Sepúlveda Cano, Lina María