Exploring New Paradigms in Time Series Prediction by Integrating Computer Simulations and Machine Learning
Time series prediction is a challenging task that requires modeling complex temporal dependencies and structural priors. In this work, we propose a novel framework that fundamentally advances existing Transformer-based methods through the introduction of the Dynamic Temporal Attention Network (DTAN)...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11080428/ |
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