Tecnicas de clasificacion para la prediccion de tarifas aereas

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1. Introducción o motivación de la tesis: Este trabajo se enfoca en los problemas multifactoriales a los que se enfrentan las aerolíneas comerciales como son la guerra de precios y la creación de una tabla dinámica de descuentos. En primer lugar, dentro de la industria aérea, los equipos de precios y ganancias pasan una cantidad de tiempo considerable analizando e interpretando las acciones de sus competidores. La mayoría de las veces, estos analistas tienen que usar sus habilidades para realizar una serie de análisis ad-hoc que les permita interpretar o encontrar patrones en las tarifas aéreas. La implementación de metodologías automáticas es clave para reducir los tiempos y evitar errores humanos. Esta tesis propone una nueva metodología para predecir, analizar e interpretar las tarifas de las aerolíneas que es capaz de imitar los procesos manuales ejecutados por los equipos de fijación precios. Para enfrentar esta guerra de precios, se propone un algoritmo de programación de expresión genética que imita el proceso manual llevado a cabo por los equipos de analistas mediante la adición automática de nuevas características o atributos. Para demostrar la capacidad de la metodología, se consideró un escenario real utilizando tarifas publicadas por parte de la aerolínea denominada Air Canada durante el período de diciembre 2019 a enero 2020; correspondiente a un período de viajes entre los meses de diciembre 2019 y abril de 2020. En segundo lugar, se aborda el problema de crear una tabla de ofertas dinámicas, debido a que, históricamente, las aerolíneas de todo el mundo han utilizado estructuras de precios estáticas, que están restringidas a puntos de precios discretos y existe una segmentación limitada entre sus pasajeros. Debido a estas limitaciones y restricciones, existe una enorme necesidad de métodos novedosos para calcular la disposición a pagar e identificar a los pasajeros potenciales, cuya probabilidad de reservar un vuelo aumenta si estos reciben un descuento con la finalidad de incrementar sus ganancias a través del incremento de las ventas de tarifas aéreas. Se propone un algoritmo de gramáticas evolutivas, el cual funciona como un selector de características para extraer los mejores subgrupos mediante el análisis del comportamiento de reservas que muestran los pasajeros. Se consideró un escenario real en el análisis experimental utilizando datos privados de una aerolínea comercial de talla mundial. 2.Contenido de la investigación: En esta tesis doctoral, se propone una metodología que tiene por objetivo la clasificación eficaz de tarifas aéreas así como la obtención de un modelo de alta interpretación. Esta metodología ha sido probada experimentalmente con tarifas reales que han sido lanzadas al público por la aerolínea Air Canada. La automatización de un modelo de clasificación, el cual es fácilmente interpretable, puede generar ganancias, evitar errores humanos, y disminuir el tiempo que se dedica a esta tarea de forma manual, permitiendo a los analistas de precios tener una perspectiva clara de lo que está ocurriendo en el mercado. A su vez, esta tesis doctoral también propone una segunda metodología enfocada a la creación de un sistema de recomendación de ofertas dinámicas que sirva como un modelo de alta interpretación basado en la tarea de descubrimiento de subgrupos (Subgroup Discovery, SD). Este modelo permite identificar subgrupos de interés para el ajuste de precios en base a las características específicas de los pasajeros, teniendo como objetivo principal el incremento de reservas de vuelos a través una página Web. Este sistema de recomendación ha sido probado experimentalmente con datos reales y privados pertenecientes una aerolínea comercial. 3.Conclusión: Las principales conclusiones obtenidas tras el desarrollo del trabajo realizado en esta tesis son las siguientes: 1. Tras hacer una búsqueda y revisión bibliográfica de la literatura relacionada acerca de los métodos que se utilizan para enfrentar la guerra de precios y los métodos de precios dinámicos dentro de la industria aérea, se ha encontrado una serie de trabajos interesantes. Esto indica que ambos problemas que se tratan de resolver en esta tesis es de actualidad y de gran importancia para las aerolíneas comerciales. De hecho, la implementación de metodologías automatizadas capaces de producir modelos de alta interpretación es de vital importancia para los equipos de analistas de precios y ganancias, debido a que se pueden reducir el número de errores humanos, aumentar la capacidad y velocidad de análisis, y la extracción de patrones o reglas interesantes. 2. La tarea de predecir y extraer conocimiento en las tarifas aéreas es una tarea muy difícil de conseguir, principalmente por dos causas: la alta cantidad de tarifas a analizar y los cambios constantes que suceden diariamente. Para solventar estas dos dificultades, en esta tesis se ha propuesto la una metodología en la cual se integra un algoritmo 96 Conclusiones y trabajo futuro de programación de expresión genética (GEP); el cual es capaz de imitar las tareas de limpieza y transformación de datos que a menudo realizan los analistas de precios dentro de una aerolínea. Por lo tanto, este algoritmo crea conjuntos de datos transformados, los cuales alimentan a un algoritmo de clasificación para predecir la clase de la tarifa a la que pertenece y extraer una serie de reglas, creando un modelo de fácil interpretación que puede ser utilizado por los analistas de precios. La metodología demostró una mejora tanto en las métricas de clasificación, como en la métrica de interpretación, siendo capaz de generar un modelo de alta interpretación. Esta metodología fue probada con un conjunto de datos reales que han sido publicados por la aerolínea bandera de Canadá (Air Canada). 3. La identificación de individuos cuya probabilidad de realizar una reserva de vuelo aumente si recibe un descuento mientras hace una búsqueda de tarifas en una página web es una tarea de enorme interés para las aerolíneas. En esta tesis doctoral, se ha propuesto una metodología que tiene como finalidad la creación de un repositorio de reglas que permitan identificar subgrupos de pasajeros, los cuales la probabilidad de efectuar una reserva de vuelo aumenta si estos reciben una oferta. Dentro de esta metodología, se ha propuesto un algoritmo de gramáticas evolutivas (GE) el cual funciona como un selector de características, creando diversos conjuntos de datos que alimentan a un algoritmo de SD para la extracción de reglas, generando un repositorio con los mejores subgrupos el cual funciona como una tabla de ofertas dinámicas y como un modelo de interpretación a la vez. La metodología demostró ser capaz de generar un repositorio de reglas únicas, evitando la redundancia de las mismas; así mismo, estos subgrupos descubiertos muestran pasajeros cuya probabilidad de efectuar una reserva aumenta si estos reciben una oferta. La metodología fue probada en un escenario real utilizando datos privados de una aerolínea comercial 4. Las dos metodologías propuestas demostraron que la utilización de técnicas de DM, como son los algoritmos de clasificación basados en reglas y los métodos de SD, en conjunto con algún tipo de algoritmo evolutivo, pueden ser muy eficientes para resolver problemas a los cuales se enfrentan las aerolíneas actualmente. 5. Este trabajo explica como se pueden crear modelos de alta interpretación, utilizando métodos de clasificación y métodos de SD, los cuales pueden ayudar a la generación de ganancias dentro de un entorno comercial altamente competitivo. 4. Bibliografía: [1] Abellán, J. and Moral, S. (2003). Building classification trees using the total uncertainty criterion. International Journal of Intelligent Systems, 18(12):1215¿1225. [2] Aggarwal, C. C. (2015). Data mining: the textbook. Springer. [3] Aggarwal, C. C., Bhuiyan, M. A., and Hasan, M. A. (2014). 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