# GraphTransformers for Geospatial Forecasting of Hurricane Trajectories

arXiv: https://arxiv.org/abs/2310.20174 · HTML: https://mlsanity.com/paper/2310.20174/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://mlsanity.com/paper/1907.00235/) (2019)
- [Predicting Hurricane Trajectories using a Recurrent Neural Network](https://mlsanity.com/paper/1802.02548/) (2018)
- [Tropical Cyclone Track Forecasting using Fused Deep Learning from Aligned Reanalysis Data](https://mlsanity.com/paper/1910.10566/) (2019)

## Abstract (arXiv)

In this paper we introduce a novel framework for trajectory prediction of geospatial sequences using GraphTransformers. When viewed across several sequences, we observed that a graph structure automatically emerges between different geospatial points that is often not taken into account for such sequence modeling tasks. We show that by leveraging this graph structure explicitly, geospatial trajectory prediction can be significantly improved. Our GraphTransformer approach improves upon state-of-the-art Transformer based baseline significantly on HURDAT, a dataset where we are interested in predicting the trajectory of a hurricane on a 6 hourly basis.