Time-LlaMA: Adapting Large Language Models for Time Series Modeling via Dynamic Low-rank Adaptation (2025.acl-srw)
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| Challenge: | Recent studies have demonstrated that large language models possess robust pattern recognition and semantic understanding capabilities over time series data. |
| Approach: | They propose a time series model that converts time series input into token embeddings and aligns time sequence embeddables with text prompts. |
| Outcome: | The proposed framework achieves the state-of-the-art (SOTA) performance and has potentials for wide industrial usages. |
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