Causal-oriented representation learning for time-series forecasting based on the spatiotemporal information transformation
Abstract In real-world high-dimensional systems, both causal dependencies and temporal information of key variables are essential for dissecting the underlying mechanisms governing system dynamics. However, effective approaches to synthesize these two interconnected aspects for deeper insights remai...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
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| Series: | Communications Physics |
| Online Access: | https://doi.org/10.1038/s42005-025-02170-6 |
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