Predicting multiple observations in complex systems through low-dimensional embeddings
Abstract Forecasting all components in complex systems is an open and challenging task, possibly due to high dimensionality and undesirable predictors. We bridge this gap by proposing a data-driven and model-free framework, namely, feature-and-reconstructed manifold mapping (FRMM), which is a combin...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-46598-w |