Ozone level forecasting in Mexico City with temporal features and interactions

Autor:	Cerritos, J. M. Sánchez, Martínez-Cadena, J. A., Marín-López, A., Delgado-Fernández, J.
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Statistics - Applications
Druh dokumentu:	Working Paper
Popis:	Tropospheric ozone is an atmospheric pollutant that negatively impacts human health and the environment. Precise estimation of ozone levels is essential for preventive measures and mitigating its effects. This work compares the accuracy of multiple regression models in forecasting ozone levels in Mexico City, first without adding temporal features and interactions, and then with these features included. Our findings show that incorporating temporal features and interactions improves the accuracy of the models. Comment: 11 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.07259 Zobrazit plný text záznamu View this record from Arxiv