TITLE: Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks 

ABSTRACT: Recurrent graph convolutional neural networks are highly effective machine
learning techniques for spatiotemporal signal processing. Newly proposed graph
neural network architectures are repetitively evaluated on standard tasks such
as traffic or weather forecasting. In this paper, we propose the Chickenpox
Cases in Hungary dataset as a new dataset for comparing graph neural network
architectures. Our time series analysis and forecasting experiments demonstrate
that the Chickenpox Cases in Hungary dataset is adequate for comparing the
predictive performance and forecasting capabilities of novel recurrent graph
neural network architectures.
Recurrent graph convolutional neural networks are highly effective machine
learning techniques for spatiotemporal signal processing. Newly proposed graph
neural network architectures are repetitively evaluated on standard tasks such
as traffic or weather forecasting. In this paper, we propose the Chickenpox
Cases in Hungary dataset as a new dataset for comparing graph neural network
architectures. Our time series analysis and forecasting experiments demonstrate
that the Chickenpox Cases in Hungary dataset is adequate for comparing the
predictive performance and forecasting capabilities of novel recurrent graph
neural network architectures.
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
http://arxiv.org/abs/2102.08100v1