Ozone level forecasting in Mexico City with temporal features and interactions

TL;DR

This paper compares regression models for forecasting Mexico City ozone levels, demonstrating that including temporal features and interactions enhances prediction accuracy for better environmental management.

Contribution

It introduces the use of temporal features and interactions in regression models to improve ozone level forecasting accuracy in Mexico City.

Findings

Inclusion of temporal features improves model accuracy

Interactions between features enhance prediction performance

Regression models with these features outperform baseline models

Abstract

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.