Dyn-GWN: Time-Series Forecasting using Time-varying Graphs with Applications to Finance and Traffic Prediction
Spatio-temporal modeling is an essential lens to understand many real-world phenomena from traffic to finance. There has been exciting work that explores spatio-temporal modeling with temporal graph convolutional networks. Often these methods assume that the spatial structure is static. We propose a...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
ACM|4th ACM International Conference on AI in Finance
2023
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| Online Access: | https://hdl.handle.net/1721.1/153137 |