Summary of Nuwats: a Foundation Model Mending Every Incomplete Time Series, by Jinguo Cheng et al.
NuwaTS: a Foundation Model Mending Every Incomplete Time Series
by Jinguo Cheng, Chunwei Yang, Wanlin Cai, Yuxuan Liang, Qingsong Wen, Yuankai Wu
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 presents NuwaTS, a novel framework for general time series imputation using Pre-trained Language Models (PLMs). Traditional models require customization for specific missing patterns or domains, limiting their applicability. Existing evaluation frameworks focus on domain-specific tasks and train/validation/test splits, failing to assess model generalization across unseen variables or domains. NuwaTS uses contrastive learning to create a versatile imputation model that can be applied to any domain. A plug-and-play fine-tuning approach enables efficient adaptation to specific tasks with minimal adjustments. The paper proposes a new benchmarking protocol for evaluating cross-variable and cross-domain generalization, showcasing NuwaTS’s superiority over state-of-the-art models on diverse datasets under this protocol. Additionally, NuwaTS generalizes well to other time series tasks like forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way to fill in missing data in time series (a sequence of numbers over time). Right now, there are many different methods for doing this that work well only for specific types of data or missing patterns. The problem with these existing methods is that they’re not very good at generalizing to new situations. To solve this issue, the authors created a new method called NuwaTS that uses pre-trained language models (like those used in text analysis) to fill in missing data. This approach allows NuwaTS to be applied to any type of time series data, making it very versatile. The paper also proposes a new way to test and evaluate these types of methods, which shows that NuwaTS is better than existing methods at filling in missing data. |
Keywords
» Artificial intelligence » Domain generalization » Fine tuning » Generalization » Time series
