Handling missing data in recurrent neural networks for air quality forecasting

Practical applications of air quality forecasting, which typically provide predictions over a horizon of hours and days, often require the handling of missing data due to unobserved relevant variables, sensor defects or communication outages. In this paper we discuss two aspects being important when building air quality forecasting models for essential air pollution parameters such as particular matter and nitrogen dioxides. Using a specialized architecture of a recurrent neural network, we can build models even if (1) unobserved variables or (2) missing data are present.