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

• URL: http://arxiv.org/abs/2102.08100v1
• Date: Tue, 16 Feb 2021 11:48:57 GMT
• Title: Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
• Authors: Benedek Rozemberczki and Paul Scherer and Oliver Kiss and Rik Sarkar and Tamas Ferenci
• Abstract summary: We propose the Chickenpox Cases dataset as a new dataset for comparing graph neural network architectures. Our time series analysis and forecasting experiments demonstrate that the Chickenpox Cases dataset is highly adequate for comparing the predictive performance forecasting capabilities of novel recurrent graph neural network architectures.
• Score: 6.037276428689637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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