Maestría en Ciencias de los Datos y Analítica (tesis)
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Ítem 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 MaríaÍtem Stock Market Forecasting : Comparing Machine Learning and Deep Learning with Risk-Return Model Selection and Evaluation in a Walk-forward Approach(Universidad EAFIT, 2024) Castro Marín, Carlos Andrés; Olarte Hernández, Tomás; Olarte Hernández, TomásThis study compares the effectiveness of machine learning and deep learning algorithms in forecasting stock market direction using daily market data of Apple Inc. stock. We aim to determine if these algorithms can identify repeatable patterns across time using price and volume history and assess which are most capable. To ensure robustness, we employ a walk-forward validation approach to maintain the temporal dimension of the data and simulate real trading conditions. This method allows us to test models across different market conditions and measure their predictive power. We prioritize model selection and evaluation based on financial return and risk metrics, focusing on profitability rather than traditional machine learning performance metrics, which often do not correlate with financial outcomes. Our findings show that traditional machine learning algorithms, specifically Random Forest, outperform deep learning models under the selected asset and conditions tested. Machine learning models exceed the stock benchmark regarding Sharpe ratio, while deep learning models struggle to manage risk effectively, leading to poorer performance. This discrepancy is likely due to the complex solution space deep learning algorithms navigate to optimize and the amount of data required by these models. However, we hypothesize that the latter could improve its performance if tested with different architectures and hyperparameters, including newly developed transformer attention-based architectures and models such as TimeGPT and others, shown in the related work section.Ítem Aplicaciones de NLP y modelos de atención para la identificación de estrés en textos de redes sociales(Universidad EAFIT, 2024) Moya Ortiz, Francisco Javier; Hernández Torres, SantiagoÍtem Non-parametric and Robust Multivariate Projection Control Charts Based on Extreme Kurtosis and Skewness Directions(Universidad EAFIT, 2024) Caicedo Jiménez, Luis Miguel; Ortiz Arias, SantiagoÍtem A Dynamic Approach to Modeling Count Data Based on Intensity Functions of Non-Homogeneous Poisson Processes and Functional Data Techniques(Universidad EAFIT, 2024) Chavarría Serna, Juan Esteban; Ortiz Arias, Santiago; Velasco, HenryÍtem Medición no invasiva de CSAT mediante técnicas de procesamiento de lenguaje natural(Universidad EAFIT, 2024) Zapata Castaño, Jonathan Stiven; Monsalve Aristizabal, Juan Daniel; Ortiz Arias, SantiagoÍtem Modelo de estimación analítica de liquidez en instituciones financieras(Universidad EAFIT, 2024) Rentería Roa, Jorge Luis; Ortiz Arias, Santiago