Summary of Lstprompt: Large Language Models As Zero-shot Time Series Forecasters by Long-short-term Prompting, By Haoxin Liu et al.
LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting
by Haoxin Liu, Zhiyuan Zhao, Jindong Wang, Harshavardhan Kamarthi, B. Aditya Prakash
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes a novel approach for zero-shot time-series forecasting (TSF) using Large Language Models (LLMs). The authors argue that existing prompting methods oversimplify TSF as language next-token predictions, failing to capture its dynamic nature. They introduce LSTPrompt, a method that decomposes TSF into short-term and long-term forecasting sub-tasks, tailoring prompts to each. This approach guides LLMs to regularly reassess forecasting mechanisms, enhancing adaptability. The authors demonstrate the effectiveness of LSTPrompt through extensive evaluations, showing consistently better performance compared to existing prompting methods and competitive results with foundation TSF models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to predict what will happen in the future based on patterns from the past. It’s like trying to guess what the weather will be tomorrow based on what it was yesterday. The researchers found that some computer programs are really good at doing this, but they need help understanding how to do it correctly. They created a new way of helping these programs called LSTPrompt. This approach breaks down predicting the future into smaller parts and gives special instructions to each part. It helps the program keep learning and adjusting as it makes predictions. The results show that this new method is better than older ways and can be used for many different kinds of prediction tasks. |
Keywords
» Artificial intelligence » Prompting » Time series » Token » Zero shot
