Uber’s Marketplace is the algorithmic brain and decision engine behind our ride-sharing services. Marketplace Forecasting builds and deploys ML algorithms to handle the immense coordination, hyperlocal decision making, and learning needed to tackle the enormous scale and movement of our transportation network. In order for our decision engines to be future-aware, we need to be able to “see into the future” as accurately as possible across both space and time. In Uber, we use H3 (a hexagonal hierarchical geospatial indexing system) to partition the data geospatially. To incorporate both geospatial and temporal features into the forecasting models in real time, we need efficient and scalable techniques for data processing. In this presentation, we provide an overview of geospatial and temporal forecasting problem in Uber Marketplace Forecasting. Then, we use real time forecasting as an example to show the need for efficient data smoothing and describe a hexagon convolution based approach for processing H3 data in production. We will demonstrate how we could incorporate hexagon convolution with Apache Flink to scale the data smooth for forecasting.