Summary of Rethinking Time Series Forecasting with Llms Via Nearest Neighbor Contrastive Learning, by Jayanie Bogahawatte et al.
Rethinking Time Series Forecasting with LLMs via Nearest Neighbor Contrastive Learning
by Jayanie Bogahawatte, Sachith Seneviratne, Maneesha Perera, Saman Halgamuge
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 This paper proposes a novel approach to adapting Large Language Models (LLMs) for time series forecasting. By customizing the input prompt using word token embeddings, the authors aim to leverage the rich representation space learned by LLMs. The proposed method, NNCL-TLLM, generates text prototypes that represent both word token embeddings and time series characteristics via end-to-end finetuning. This approach is then fine-tuned for layer normalization and positional embeddings of the LLM, reducing trainable parameters and computational cost. Experiments show that NNCL-TLLM outperforms state-of-the-art methods in few-shot forecasting and achieves competitive performance in long-term and short-term forecasting tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us forecast time series data better by using special language models called Large Language Models (LLMs). The problem is to make the LLMs understand what we want them to predict, like future stock prices or weather patterns. The authors found a way to do this by creating text prototypes that have both the words and the characteristics of the time series data. They tested their approach on different forecasting tasks and showed that it works better than other methods in some cases. |
Keywords
» Artificial intelligence » Few shot » Prompt » Time series » Token
