I have been hacking on a peroneal research project to predict hurricane tracks furcating using deep learning. Only given track and intensity data at different points in time (every 6 hours) and some simple feature engineering, you will not get any good results close to the official NHC forecast, no matter what model you use.
In hindsight, this is a little obvious. Hurricane forecasting time series models depend more on other factors than time itself. A sales forecast can depend on seasonal trends and key events in time, but a hurricane forecast is much more dependent on long-range spatial data like the state atmosphere and ocean that are very non-trivial to model simply using just track data.
However, deep leading models and techniques in this scenario are helpful because they can allow you to integrate multiple modalities like images, graphs, and volumetric data into this one model, which may not be possible with statistical models alone.