在数据少的尼日利亚北部，简单模型比深度学习更准
When Simpler Wins: Facebooks Prophet vs LSTM for Air Pollution Forecasting in Data-Constrained Northern Nigeria
- 用月度数据对比LSTM和Facebook Prophet的污染预测效果
- Prophet在有季节性趋势时表现更好，甚至超过LSTM
- 适合资源有限地区选模型时优先考虑简单高效方法
空气污染预测对主动环境管理至关重要，但低资源地区普遍存在数据不规则和匮乏问题。尼日利亚北部污染水平高，但针对该地区在数据受限条件下先进机器学习模型性能的系统比较研究较少。本研究基于2018至2023年19个州的月度观测数据，评估了长短期记忆网络（LSTM）与Facebook Prophet模型对多种污染物（CO、SO2、SO4）的预测表现。结果表明，Prophet在以季节性和长期趋势为主的时间序列中通常达到或优于LSTM的准确性；而LSTM在具有突发结构变化的数据集上表现更优。研究挑战了‘深度学习模型必然优于简单方法’的假设，强调模型与数据特征的匹配至关重要。对资源受限地区的政策制定者和实践者而言，本研究支持采用上下文敏感、计算高效的预测方法，而非盲目追求复杂性。
原文摘要 · Abstract (English)
Air pollution forecasting is critical for proactive environmental management, yet data irregularities and scarcity remain major challenges in low-resource regions. Northern Nigeria faces high levels of air pollutants, but few studies have systematically compared the performance of advanced machine learning models under such constraints. This study evaluates Long Short-Term Memory (LSTM) networks and the Facebook Prophet model for forecasting multiple pollutants (CO, SO2, SO4) using monthly observational data from 2018 to 2023 across 19 states. Results show that Prophet often matches or exceeds LSTM's accuracy, particularly in series dominated by seasonal and long-term trends, while LSTM performs better in datasets with abrupt structural changes. These findings challenge the assumption that deep learning models inherently outperform simpler approaches, highlighting the importance of model-data alignment. For policymakers and practitioners in resource-constrained settings, this work supports adopting context-sensitive, computationally efficient forecasting methods over complexity for its own sake.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。