arXiv cs.AI· Moazzam Umer Gondal, Hamad ul Qudous, Asma Ahmad Farhan, Sultan Alamri·· 13 小时前AI 评分18
北京PM2.5小时级预测对比研究：SARIMAX、Facebook Prophet与NeuralProphet的自适应时序模型评测
Interpretable PM2.5 Forecasting for Urban Air Quality: A Comparative Study of Adaptive Time-Series Models
AI 导读
一项针对北京小时级PM2.5预测的研究对比了SARIMAX、Facebook Prophet和NeuralProphet三种轻量可解释时序模型。
正文 · 原文
Abstract:Accurate short-term air-quality forecasting is essential for public health protection and urban management, yet many recent forecasting frameworks rely on complex, data-intensive, and computationally demanding models. This study investigates whether lightweight and interpretable forecasting approaches can provide competitive performance for hourly PM2.5 prediction in Beijing, China. Using multi-year pollutant and meteorological time-series data, we developed a leakage-aware forecasting workflow that combined chronological data partitioning, preprocessing, feature selection, and exogenous-driver modeling under the Perfect Prognosis setting. Three forecasting families were evaluated: SARIMAX, Facebook Prophet, and NeuralProphet. To assess practical deployment behavior, the models were tested under two adaptive regimes: weekly walk-forward refitting and frozen forecasting with online residual correction. Results showed clear differences in both predictive accuracy and computational efficiency. Under walk-forward refitting, Facebook Prophet achieved the strongest completed performance, with an MAE of $37.61$ and an RMSE of $50.10$, while also requiring substantially less execution time than NeuralProphet. In the frozen-model regime, online residual correction improved Facebook Prophet and SARIMAX, with corrected SARIMAX yielding the lowest overall error (MAE $32.50$; RMSE $46.85$). NeuralProphet remained less accurate and less stable across both regimes, and residual correction did not improve its forecasts. Notably, corrected Facebook Prophet reached nearly the same error as its walk-forward counterpart while reducing runtime from $15$ min $21.91$ sec to $46.60$ sec. These findings show that lightweight additive forecasting strategies can remain highly competitive for urban air-quality prediction, offering a practical balance between accuracy, interpretability, ...
| Comments: | Submitted to Scientific Reports |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.25495 [cs.LG] |
| (or arXiv:2603.25495v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.25495 arXiv-issued DOI via DataCite |
Submission history
From: Moazzam Umer Gondal [view email]
[v1]
Thu, 26 Mar 2026 14:35:43 UTC (8,706 KB)
[v2]
Mon, 19 Oct 2026 21:47:54 UTC (121 KB)
来源：arXiv cs.AI · arxiv.org