通过解耦时空因果关系，提升空气质量预测精度
Spatiotemporal Causal Decoupling Model for Air Quality Forecasting
- 构建时空模块与知识嵌入结合的因果解耦框架
- 在公开数据集上相比顶尖模型提升超20%预测准确率
- 适合关注空气质量建模与因果推理的研究者
由于空气污染对人类健康、生活和经济发展具有深远影响，空气质量预测至关重要。我们首先采用因果图方法分析现有研究在全面建模空气质量指数（AQI）与气象特征之间因果关系时的局限性。为提高预测精度，提出新模型AirCade，引入因果解耦机制。AirCade结合时空模块与知识嵌入技术，捕捉AQI内部动态；进一步设计因果解耦模块，将同步因果关系从历史AQI与气象特征中分离，并将获取的知识传播至未来时间步以增强性能。此外，引入因果干预机制，显式表征未来气象特征的不确定性，提升模型鲁棒性。在公开空气质量数据集上的评估显示，AirCade相较最先进模型实现超过20%的相对改进。
原文摘要 · Abstract (English)
Due to the profound impact of air pollution on human health, livelihoods, and economic development, air quality forecasting is of paramount significance. Initially, we employ the causal graph method to scrutinize the constraints of existing research in comprehensively modeling the causal relationships between the air quality index (AQI) and meteorological features. In order to enhance prediction accuracy, we introduce a novel air quality forecasting model, AirCade, which incorporates a causal decoupling approach. AirCade leverages a spatiotemporal module in conjunction with knowledge embedding techniques to capture the internal dynamics of AQI. Subsequently, a causal decoupling module is proposed to disentangle synchronous causality from past AQI and meteorological features, followed by the dissemination of acquired knowledge to future time steps to enhance performance. Additionally, we introduce a causal intervention mechanism to explicitly represent the uncertainty of future meteorological features, thereby bolstering the model's robustness. Our evaluation of AirCade on an open-source air quality dataset demonstrates over 20\% relative improvement over state-of-the-art models.
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。