核心概念
This paper presents a novel forecasting methodology that leverages both past and future covariates, such as weather forecasts and calendar events, to accurately predict NO2 concentrations using Spatiotemporal Graph Neural Networks (STGNNs).
摘要
The paper focuses on the problem of air pollution forecasting, specifically predicting NO2 concentrations. It proposes a novel forecasting approach that utilizes both past and future covariates, in contrast to existing methods that only consider past covariates.
Key highlights:
- The authors model the underlying data structure as a graph, where monitoring stations are represented as nodes and their dependencies as edges. This allows them to capture the spatiotemporal dynamics of the air quality observations.
- The proposed method incorporates a conditioning block that embeds past and future covariates, such as traffic data and weather forecasts, into the current observations. This additional information is then fused and projected into the forecasting horizon to generate the final prediction.
- The authors find that conditioning on future weather information has a greater impact on prediction performance than considering past traffic conditions.
- Experiments on a real-world air quality dataset show that the proposed approach outperforms existing state-of-the-art forecasting methods, demonstrating superior performance in predicting NO2 concentrations up to a 3-day forecasting horizon.
統計資料
Traffic data, such as the number of vehicles and their speed, are closely linked to air quality conditions.
Past and future weather conditions, including temperature, humidity, precipitation, and wind speed, can significantly influence the dispersion and deposition of air pollutants.
引述
"To the best of our knowledge, it is the first time that future covariates are included in time series predictions in a structured way."
"Remarkably, we find that conditioning on future weather information has a greater impact than considering past traffic conditions."