
Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data - jonbaer
https://arxiv.org/abs/1909.04019v3
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fghorow
I 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.

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dave-anderson
The 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.

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Tarq0n
The 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.

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dave-anderson
The 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_co...](http://www-
scf.usc.edu/~yaguang/papers/aaai19_multi_graph_convolution.pdf) 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.

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toddaustin
Interesting paper. Adapt the Transformer to spatiotemporal forecasting for the
first time. SOTA results in taxi demand forecasting.

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jacobhk2
This 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.

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deehouie
Any implementation yet?

