If you&#x27;re in domain of, say, weather forecasting. You need a model correlating a number of variables both spatially and temporary. Spatial data is kind of hard to work with, and apparently common approaches hasn&#x27;t incorporated time very well, focusing mostly on problems related to problems of space, so they improved it.<p>Example of a problem, you have a set of sensors at various geographical locations. At some points between sensors you need to forecast a measure, say temperature.
Even just interpolating a current value can be challenging, forecasting in time doesn&#x27;t make it easier.