Automatically Inferring Data Quality for Spatiotemporal Forecasting

ICLR 2018 Sungyong SeoArash MoheghGeorge Ban-WeissYan Liu

Spatiotemporal forecasting has become an increasingly important prediction task in machine learning and statistics due to its vast applications, such as climate modeling, traffic prediction, video caching predictions, and so on. While numerous studies have been conducted, most existing works assume that the data from different sources or across different locations are equally reliable... (read more)

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