用时间特征提升墨西哥城臭氧预测精度
Ozone level forecasting in Mexico City with temporal features and interactions
- 引入时间特征与交互项改进预测模型
- 加入时间因素后模型准确率显著提高
- 适合环境监测与公共健康研究者参考
对流层臭氧是一种危害人类健康与环境的污染物，精准预测其浓度对预防措施至关重要。本文对比了多种回归模型在墨西哥城臭氧水平预测中的表现，先在不引入时间特征与交互项的情况下测试，再加入这些特征进行比较。结果表明，引入时间特征与交互作用能有效提升模型预测准确性。
原文摘要 · Abstract (English)
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.
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