Deep Learning for Spatiotemporal Anomaly Forecasting: A Case Study of Marine Heatwaves

Spatiotemporal data have unique properties and require specific considerations. Forecasting spatiotemporal processes is a difficult task because the data are high-dimensional, often are limited in extent, and temporally correlated. Hence, we propose to evaluate several deep learning-based approaches that are relevant to spatiotemporal anomaly forecasting. We will use marine heatwaves as a case study. Those are observed around the world and have strong impacts on marine ecosystems. The evaluated deep learning methods will be integrated for the task of marine heatwave prediction in order to overcome the limitations of spatiotemporal data and improve data-driven seasonal marine heatwave forecasts