Summary of Time-ffm: Towards Lm-empowered Federated Foundation Model For Time Series Forecasting, by Qingxiang Liu et al.
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting
by Qingxiang Liu, Xu Liu, Chenghao Liu, Qingsong Wen, Yuxuan Liang
First submitted to arxiv on: 23 May 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 paper proposes Time-FFM, a Federated Foundation Model for time series forecasting by leveraging pre-trained language models (LMs). Unlike traditional approaches that require large amounts of data, Time-FFM addresses the challenge of developing Foundation Models (FMs) for time series forecasting due to data scarcity. To achieve this, the authors transform time series into text tokens and propose a prompt adaptation module to determine domain-customized prompts dynamically. The model also incorporates a personalized federated training strategy by learning global encoders and local prediction heads to handle heterogeneous data across domains. Experimental results show that Time-FFM outperforms state-of-the-art models, demonstrating effective few-shot and zero-shot forecasting capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to improve time series forecasting, which is important for predicting things like stock prices or weather patterns. The problem is that there isn’t enough data to train a good model. To fix this, the authors create a new type of model called Time-FFM that uses language models to analyze time series data. They also develop a way to customize prompts for different types of forecasting tasks and use a special training method to handle differences in the data between these tasks. The results show that their approach is better than other methods, which could be useful for making predictions. |
Keywords
» Artificial intelligence » Few shot » Prompt » Time series » Zero shot
