  We present a series of modifications which improve upon Graph WaveNet's
previously state-of-the-art performance on the METR-LA traffic prediction task.
The goal of this task is to predict the future speed of traffic at each sensor
in a network using the past hour of sensor readings. Graph WaveNet (GWN) is a
spatio-temporal graph neural network which interleaves graph convolution to
aggregate information from nearby sensors and dilated convolutions to aggregate
information from the past. We improve GWN by (1) using better hyperparameters,
(2) adding connections that allow larger gradients to flow back to the early
convolutional layers, and (3) pretraining on an easier short-term traffic
prediction task. These modifications reduce the mean absolute error by .06 on
the METR-LA task, nearly equal to GWN's improvement over its predecessor. These
improvements generalize to the PEMS-BAY dataset, with similar relative
magnitude. We also show that ensembling separate models for short-and long-term
predictions further improves performance. Code is available at
https://github.com/sshleifer/Graph-WaveNet .
