The New Era of Dynamic Pricing: Synergizing Supervised Learning and Quadratic Programming
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866929254174818304 |
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| author | Bramao, Gustavo Tarygin, Ilia |
| author_facet | Bramao, Gustavo Tarygin, Ilia |
| contents | In this paper, we explore a novel combination of supervised learning and quadratic programming to refine dynamic pricing models in the car rental industry. We utilize dynamic modeling of price elasticity, informed by ordinary least squares (OLS) metrics such as p-values, homoscedasticity, error normality. These metrics, when their underlying assumptions hold, are integral in guiding a quadratic programming agent. The program is tasked with optimizing margin for a given finite set target. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14844 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The New Era of Dynamic Pricing: Synergizing Supervised Learning and Quadratic Programming Bramao, Gustavo Tarygin, Ilia Optimization and Control Machine Learning In this paper, we explore a novel combination of supervised learning and quadratic programming to refine dynamic pricing models in the car rental industry. We utilize dynamic modeling of price elasticity, informed by ordinary least squares (OLS) metrics such as p-values, homoscedasticity, error normality. These metrics, when their underlying assumptions hold, are integral in guiding a quadratic programming agent. The program is tasked with optimizing margin for a given finite set target. |
| title | The New Era of Dynamic Pricing: Synergizing Supervised Learning and Quadratic Programming |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2402.14844 |