Examinando por Materia "Prediction"
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Publicación Competencias que mejor predicen la calidad del desempeño de las personas que trabajan en organizaciones E-commerce colombiana(Universidad EAFIT, 2022) Orduz Gómez, Natalia; Granados Gómez, Patricia Diana; Sanín Posada, John AlejandroCompetencies have the ability to predict and explain the quality of performance. In turn, performance is a predictor of the organization's productivity. This also happens in E-commerce companies. Taking into account that in Colombia this type of company generates a significant proportion of employment and it is expected that it will increase in the future. The objective of this research is to explore the competencies that best predict the quality of performance of the people who work in this type of organization. To find out about them, (eight) E-commerce experts with experience of 2 or more years in the HR area were interviewed. Through content analysis, emerging categories were built that allowed discovering the competencies that best predict the quality of performance of the people who work in these organizations in Colombia. At the end, it is discussed how these findings contribute to E-commerce organizations, organizational leaders, collaborators and human resources areas, as input for selection processes, and training.Ítem Forecasting of time series with trend and seasonal cycle using the airline model and artificial neural networks(Universidad EAFIT, 2012-06-15) Velásquez, J D; Franco, C J; Universidad Nacional de ColombiaPublicación Hurto a personas en la ciudad de Medellín : análisis predictivo de la cantidad de casos en diferentes zonas de la ciudad a partir de modelos de machine learning implementando técnicas de MLOps(Universidad EAFIT, 2023) Arboleda Colorado, Jeferson Stiven; Martínez Vargas, Juan DavidRobbery of individuals in Medellín is an issue demanding immediate attention. This prompted the study of the phenomenon within an analytics project, spanning data collection, database construction, modeling, and production deployment. It's worth noting that MLOps methodology was employed utilizing AWS services. Visual tools related to the phenomenon were integrated, facilitating analysis.Publicación Incorporating a predictive component in a dynamic segmentation approach(Universidad EAFIT, 2021) Saldarriaga Aristizábal, Pablo Andrés; Laniado, Heny; Monroy, Juan CarlosPublicación Integración de modelos estadísticos y de aprendizaje automático para predecir y mitigar la rotación voluntaria de empleados(Universidad EAFIT, 2025) González Ruiz, John Jairo; Almonacid Hurtado, Paula MaríaPublicación Predicción de alteraciones nutricionales en función del índice peso para la talla en niños menores de 5 años, de la ciudad de Medellín(Universidad EAFIT, 2023) Bedoya Ríos, Santiago; Martínez Vargas, Juan David; Sepúlveda Cano, Lina MaríaPublicación Predicción de direcciones de activos financieros basados en la volatilidad en series temporales utilizando machine learning(Universidad EAFIT, 2026) Holguín Carvalho, Mateo; Velasco Vera, Henry GiovannyIdentifying effective trading signals in financial assets is a challenge that draws attention across multiple disciplines due to the volatile and dynamic nature of financial markets. The complexity investors face stems from the wide range of factors that influence asset prices, including macroeconomic variables, corporate decisions, and unexpected events, making it difficult to obtain precise estimates of future movements. This is particularly relevant for investors seeking to build portfolios that maximize returns. In this context, some variables exhibit stronger relationships with market-driven factors, making them useful indicators for anticipating price direction. Nevertheless, recent advances in computing and in Machine Learning and Deep Learning techniques have enabled the development of more sophisticated models that facilitate this task. This study compares time-series-based machine learning methodologies, specifically LSTM neural networks and LightGBM decision-tree models, while incorporating Conditional Heteroskedasticity models (GARCH) to improve the classification of buy and sell signals in financial instruments, accounting for both historical patterns and external variables affecting asset behavior. The results show that LightGBM achieved the best predictive performance, with notable metrics such as an F1 Score of 0.823 and an AUC-ROC of 0.923 in validation, whereas LSTM delivered the best financial performance, reaching a cumulative return of 28.05% and a Sharpe Ratio of 0.70, clearly outperforming a Buy-and-Hold strategy. These findings suggest that although daily directional prediction is inherently complex, advanced Machine Learning models can transform weak signals into profitable trading strategies.Publicación Predicting the entrepreneurial process phases : a machine learning approach(Universidad EAFIT, 2021) Ceballos Arias, Juan Camilo; Álvarez Barrera, Claudia Patricia; Almonacid Hurtado, Paula María