Neural network study for the subject demand forecasting
Course planning of an educative center or university is composed of multiple complex problems like the design of the schedule for the students, classrooms, and professors for each signature. One of the problems is the forecasting of the number of subjects to make available for the students; this pro...
| Main Authors: | , , , , , , |
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| Format: | Online |
| Language: | spa |
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Universidad Pedagógica y Tecnológica de Colombia
2019
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| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/8783 |
| _version_ | 1801706095030304768 |
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| author | Terán-Villanueva, Jesús David Ibarra-Martínez, Salvador Laria-Menchaca, Julio Castán-Rocha, José Antonio Treviño-Berrones, Mayra Guadalupe García-Ruiz, Alejandro Humberto Martínez-Infante, José Eduardo |
| author_facet | Terán-Villanueva, Jesús David Ibarra-Martínez, Salvador Laria-Menchaca, Julio Castán-Rocha, José Antonio Treviño-Berrones, Mayra Guadalupe García-Ruiz, Alejandro Humberto Martínez-Infante, José Eduardo |
| author_sort | Terán-Villanueva, Jesús David |
| collection | OJS |
| description | Course planning of an educative center or university is composed of multiple complex problems like the design of the schedule for the students, classrooms, and professors for each signature. One of the problems is the forecasting of the number of subjects to make available for the students; this problem seems easy at first glance because once we have the number of approved and failed students for each subject, we can easily calculate the following demand for each subject. However, there are occasions where the course planning for the following period starts before having the information related to the number of accredited students; which lead us to the problem of forecasting the accreditation ratio for the calculation of the subject demand from the students. In this paper, the performance of a causal model compares to the performance of a statistical model for the forecasting of the approve and fail ratio of the students. The final results show that the causal model outperforms the statistical model for the given instances. We consider that this advantage occurs because the causal model learns the behavior patterns of the training data independently, instead of generalizing the accreditation ratio. Additionally, the statistical method can present significant problems when trying to forecast accreditation ratios for situations that are not found in the training data, while the causal model will use the information learned to predict such situations. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-8783 |
| institution | Revista Facultad de Ingeniería |
| language | spa |
| publishDate | 2019 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-87832021-07-13T02:26:26Z Neural network study for the subject demand forecasting Estudio de redes neuronales para el pronóstico de la demanda de asignaturas Terán-Villanueva, Jesús David Ibarra-Martínez, Salvador Laria-Menchaca, Julio Castán-Rocha, José Antonio Treviño-Berrones, Mayra Guadalupe García-Ruiz, Alejandro Humberto Martínez-Infante, José Eduardo artificial neural networks demand forecasting strategic planning planeación estratégica pronóstico de demanda redes neuronales artificiales Course planning of an educative center or university is composed of multiple complex problems like the design of the schedule for the students, classrooms, and professors for each signature. One of the problems is the forecasting of the number of subjects to make available for the students; this problem seems easy at first glance because once we have the number of approved and failed students for each subject, we can easily calculate the following demand for each subject. However, there are occasions where the course planning for the following period starts before having the information related to the number of accredited students; which lead us to the problem of forecasting the accreditation ratio for the calculation of the subject demand from the students. In this paper, the performance of a causal model compares to the performance of a statistical model for the forecasting of the approve and fail ratio of the students. The final results show that the causal model outperforms the statistical model for the given instances. We consider that this advantage occurs because the causal model learns the behavior patterns of the training data independently, instead of generalizing the accreditation ratio. Additionally, the statistical method can present significant problems when trying to forecast accreditation ratios for situations that are not found in the training data, while the causal model will use the information learned to predict such situations. La planeación de cursos de un centro educativo o universidad está compuesta por múltiples problemas complejos como lo es la asignación de horarios para los alumnos, salones y profesores para cada asignatura. Uno de los problemas iniciales es determinar la cantidad de asignaturas que se ofertarán; este problema parece sencillo a simple vista ya que una vez que se tenga la información de la cantidad de alumnos aprobados para cada asignatura, se puede calcular fácilmente la siguiente demanda de asignaturas. Sin embargo, existen ocasiones en los que la planeación de cursos del siguiente período inicia antes de tener la información relativa a la aprobación de los alumnos. Lo cual nos lleva al problema del pronóstico de los porcentajes de aprobación para calcular la demanda de asignaturas de los alumnos. En este trabajo se compara el desempeño de modelos causales contra modelos estadísticos para el pronóstico de los porcentajes de aprobación y reprobación de los alumnos. Los resultados finales muestran una ventaja importante de los métodos causales sobre los métodos estadísticos para los casos de prueba. Consideramos que esta ventaja ocurre debido a que el modelo causal aprende los patrones de comportamiento de los datos de entrenamiento de forma independiente en vez de generalizar porcentajes de acreditación. Además de lo anterior, el método estadístico puede presentar problemas importantes al tratar de pronosticar porcentajes de acreditación para situaciones que no se encuentren en los datos de entrenamiento, mientras que el modelo causal utilizará la información aprendida para pronosticar dichas situaciones. Universidad Pedagógica y Tecnológica de Colombia 2019-01-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion research investigación application/pdf application/xml https://revistas.uptc.edu.co/index.php/ingenieria/article/view/8783 10.19053/01211129.v28.n50.2019.8783 Revista Facultad de Ingeniería; Vol. 28 No. 50 (2019); 34-43 Revista Facultad de Ingeniería; Vol. 28 Núm. 50 (2019); 34-43 2357-5328 0121-1129 spa https://revistas.uptc.edu.co/index.php/ingenieria/article/view/8783/7285 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/8783/7502 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/8783/7531 N.A. N.A. |
| spellingShingle | artificial neural networks demand forecasting strategic planning planeación estratégica pronóstico de demanda redes neuronales artificiales Terán-Villanueva, Jesús David Ibarra-Martínez, Salvador Laria-Menchaca, Julio Castán-Rocha, José Antonio Treviño-Berrones, Mayra Guadalupe García-Ruiz, Alejandro Humberto Martínez-Infante, José Eduardo Neural network study for the subject demand forecasting |
| title | Neural network study for the subject demand forecasting |
| title_alt | Estudio de redes neuronales para el pronóstico de la demanda de asignaturas |
| title_full | Neural network study for the subject demand forecasting |
| title_fullStr | Neural network study for the subject demand forecasting |
| title_full_unstemmed | Neural network study for the subject demand forecasting |
| title_short | Neural network study for the subject demand forecasting |
| title_sort | neural network study for the subject demand forecasting |
| topic | artificial neural networks demand forecasting strategic planning planeación estratégica pronóstico de demanda redes neuronales artificiales |
| topic_facet | artificial neural networks demand forecasting strategic planning planeación estratégica pronóstico de demanda redes neuronales artificiales |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/8783 |
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