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Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data
- fghorow 7y agoI have only read the abstract of this paper so far. But the idea of minimal graphs representing the dynamics of a system goes back (at least) to the work of Jim Crutchfield [http://csc.ucdavis.edu/~chaos/ http://csc.ucdavis.edu/~chaos/] in the '80s.
- domnomnom 7y agoThis is very cool stuff. I really appreciate the link.
- dave-anderson 7y agoThe paper is really novel in terms of embedding the minimal graph into the Transformer for time series forecasting. As far as I know, there is no paper having done that before.
- deehouie 7y agoAny implementation yet?
- Tarq0n 7y agoThe baselines they compare to are only other neural network models, rather than models that actually state of the art for time series. This makes the author's abstract rather disingenuous.
- dave-anderson 7y agoThe paper has no problem there at all. Many prior papers have demonstrated that neural network models significantly outperform non-neural network models in transportation forecasting like https://arxiv.org/abs/1707.01926 https://arxiv.org/abs/1707.01926, http://www-scf.usc.edu/~yaguang/papers/aaai19_multi_graph_convolution.pdf http://www-scf.usc.edu/~yaguang/papers/aaai19_multi_graph_co... As this paper has chosen the state-of-the-art neural network models as baselines, it has already done enough and decent jobs on choosing baselines.
- jacobhk2 7y agoThis paper presents a rather interesting idea that the Transformer, an NLP model, can be modified for spatio-temporal prediction. The first work on applying the Transformer to this domain.
- toddaustin 7y agoInteresting paper. Adapt the Transformer to spatiotemporal forecasting for the first time. SOTA results in taxi demand forecasting.