The air pollution problem has been a serious issue for public health and city development in recent years, which rises an urgent demand for accurate air pollution prediction models. Traditional time series prediction scheme has been challenged in such case because the air pollution signals can be highly dynamic and uncertain. Distinct latent physical processes and complex environment changes over time make it hard for one fixed model to give consistently good performance. Also probabilistic prediction and uncertainty-aware estimation are critical when the model is used for public decisions in practice. However, few previous works can simultaneously provide adaptive and probabilistic air pollution prediction abilities, which are important practical issues for the real-world implementation of data-driven air pollution prediction models. In this paper we propose an adversarial meta learning approach to address these concerns. After reformulating the target signals as a collection of independent but similar mini-tasks along time, we adversarially learn a meta model that includes an implicit generator to adaptively give more informative probabilistic predictions across tasks. We further provide a bayesian meta learning interpretation that recasts the proposed model as an approximate minimizer for the Wasserstein distance between a generative latent model and the true data distribution. Our model provides an algorithm that explicitly models how the prediction distribution is conditioned on underlying data patterns and simultaneously gives adaptive uncertainty estimation. Experiments on both synthetic and real-world air pollution datasets show that the proposed model can simultaneously provide better probabilistic and adaptive prediction while keeping stratifying point prediction error.INDEX TERMS Air pollution prediction, meta learning, probabilistic forecast, adversarial networks.

Air pollution is a significant public concern worldwide, and accurate data-driven air pollution prediction is crucial for developing alerting systems and making urban decisions. As more and more cities establish their monitoring networks, there is a pressing need for coldstart model training with limited data accumulation in new cities. However, traditional spatial-temporal modeling and transfer learning schemes have been challenged under this scenario because of insufficient usage of available source data and suboptimal transferring strategy. To address these issues, we propose a meta-learning-based spatial-temporal adaptation solution for coldstart air pollution prediction. Our approach is a model-agnostic framework that enables a given backbone predictor with adaption ability across different space and time locations. Specifically, it learns a factorization of the available source data distribution and recognizes the target city as one of its components, greatly reducing the data accumulation requirement and providing coldstart capability. Furthermore, we design a novel bidirectional meta-learner that can simultaneously leverage task embeddings learned from data and features constructed based on prior knowledge. We conduct comprehensive experiments on both synthetic and real-world air pollution datasets of four distinct pollutants. The results demonstrate that our proposed method achieves a 5.2% lower 24-hour prediction mean absolute error (MAE) than pretraining and fine-tuning solutions when facing a new city with only 200 hours of data, which empirically verifies the effectiveness of our approach as a coldstart training solution.

No abstract