Summary of Multi-patch Prediction: Adapting Llms For Time Series Representation Learning, by Yuxuan Bian et al.
Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning
by Yuxuan Bian, Xuan Ju, Jiangtong Li, Zhijian Xu, Dawei Cheng, Qiang Xu
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed aLLM4TS framework adapts Large Language Models (LLMs) for time-series representation learning by reconceiving forecasting as a self-supervised, multi-patch prediction task. This approach captures temporal dynamics more effectively than traditional methods. The framework consists of two-stage training: causal continual pre-training on various datasets and fine-tuning for multi-patch prediction in the target context. A patch-wise decoding layer is introduced to directly transpose individual patches into temporal sequences, enhancing the model’s proficiency in mastering temporal patch-based representations. aLLM4TS demonstrates superior performance in several downstream tasks, showing its effectiveness in deriving temporal representations with enhanced transferability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new way for computers to understand time-series data. They do this by treating forecasting as a puzzle where the computer predicts what comes next in a sequence of numbers. This approach works better than previous methods and helps the computer learn more about patterns in the data. The researchers also add a special layer that helps the computer turn individual pieces of the sequence into a complete picture. This new method performs well on several tasks, showing its ability to understand time-series data. |
Keywords
* Artificial intelligence * Fine tuning * Representation learning * Self supervised * Time series * Transferability
