Wang, Y;
Aste, T;
(2022)
Network Filtering of Spatial-temporal GNN for Multivariate Time-series Prediction.
In:
Proceedings of the 3rd ACM International Conference on AI in Finance, ICAIF 2022.
(pp. pp. 463-470).
ACM
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Abstract
We propose an architecture for multivariate time-series prediction that integrates a spatial-temporal graph neural network with a filtering module which filters the inverse correlation matrix into a sparse network structure. In contrast with existing sparsification methods adopted in graph neural networks, our model explicitly leverages time-series filtering to overcome the low signal-to-noise ratio typical of complex systems data. We present a set of experiments, where we predict future sales volume from a synthetic time-series sales volume dataset. The proposed spatial-temporal graph neural network displays superior performances to baseline approaches with no graphical information, fully connected, disconnected graphs, and unfiltered graphs, as well as the state-of-the-art spatial-temporal GNN. Comparison of the results with Diffusion Convolutional Recurrent Neural Network (DCRNN) suggests that, by combining a (inferior) GNN with graph sparsification and filtering, one can achieve comparable or better efficacy than the state-of-the-art in multivariate time-series regression.
| Type: | Proceedings paper |
|---|---|
| Title: | Network Filtering of Spatial-temporal GNN for Multivariate Time-series Prediction |
| Event: | ICAIF '22: Third ACM International Conference on AI in Finance |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3533271.3561678 |
| Publisher version: | https://doi.org/10.1145/3533271.3561678 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Spatial-temporal GNN, LSTM, Attention, Sparse Graph, Complex Network, Correlation Matrix, Information Filtering Network, Multivariate, Time-series Forecasting |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10167562 |
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