Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
This provides a new benchmark dataset for researchers in spatiotemporal signal processing, but it is incremental as it adds to existing datasets without solving a broader problem.
The authors tackled the lack of diverse datasets for evaluating graph neural networks by introducing the Chickenpox Cases in Hungary dataset, and their experiments showed it is adequate for comparing predictive performance and forecasting capabilities of such 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.