轻量级模型在空气质量预测中表现优于深度学习，兼具高精度与可解释性。
Beyond the Hype: Comparing Lightweight and Deep Learning Models for Air Quality Forecasting
- 采用因果相关性与互信息筛选特征，用时间顺序划分数据避免泄露。
- Facebook Prophet 在北京PM2.5和PM10预测上测试R²均超0.94，优于其他模型。
- 适合需要透明、易部署的环境政策决策场景，如城市污染预警系统。
准确预测城市空气污染对保护公众健康和指导减排政策至关重要。尽管深度学习与混合模型近年占据主导，但其复杂性和低可解释性限制了实际应用。本研究评估轻量级加法模型——Facebook Prophet（FBP）和NeuralProphet（NP）——在北京市细颗粒物（PM₂.₅、PM₁₀）预测中的表现。基于多年污染物与气象数据，采用系统特征选择（相关性、互信息、mRMR）、防泄露归一化及时间序列分割策略。两模型使用污染物及其前体物作为输入，NeuralProphet还引入滞后依赖。对比基准包括两种机器学习模型（LSTM、LightGBM）和一种传统统计模型（SARIMAX）。使用7天预留集进行评估，指标为MAE、RMSE和R²。结果表明，FBP始终优于NP、SARIMAX及学习型基线，在两种污染物上的测试R²均超过0.94。研究证明，可解释的加法模型在精度上仍可媲美传统与复杂方法，兼具准确性、透明性与部署便捷性。
原文摘要 · Abstract (English)
Accurate forecasting of urban air pollution is essential for protecting public health and guiding mitigation policies. While Deep Learning (DL) and hybrid pipelines dominate recent research, their complexity and limited interpretability hinder operational use. This study investigates whether lightweight additive models -- Facebook Prophet (FBP) and NeuralProphet (NP) -- can deliver competitive forecasts for particulate matter (PM$_{2.5}$, PM$_{10}$) in Beijing, China. Using multi-year pollutant and meteorological data, we applied systematic feature selection (correlation, mutual information, mRMR), leakage-safe scaling, and chronological data splits. Both models were trained with pollutant and precursor regressors, with NP additionally leveraging lagged dependencies. For context, two machine learning baselines (LSTM, LightGBM) and one traditional statistical model (SARIMAX) were also implemented. Performance was evaluated on a 7-day holdout using MAE, RMSE, and $R^2$. Results show that FBP consistently outperformed NP, SARIMAX, and the learning-based baselines, achieving test $R^2$ above 0.94 for both pollutants. These findings demonstrate that interpretable additive models remain competitive with both traditional and complex approaches, offering a practical balance of accuracy, transparency, and ease of deployment.
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