Summary of Vitime: a Visual Intelligence-based Foundation Model For Time Series Forecasting, by Luoxiao Yang et al.
ViTime: A Visual Intelligence-Based Foundation Model for Time Series Forecasting
by Luoxiao Yang, Yun Wang, Xinqi Fan, Israel Cohen, Jingdong Chen, Yue Zhao, Zijun Zhang
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 A pioneering study in developing a time series forecasting (TSF) foundation model proposes a vision intelligence-powered framework, ViTime, for the first time. The framework synthesizes authentic time series periodic and trend patterns to enrich sample pattern diversity and operates TSF in image metric space to achieve significantly enhanced generalizability. The paper demonstrates ViTime’s state-of-the-art performance across multiple settings, outperforming TimesFM by 9-15% in zero-shot scenarios and surpassing both foundation models and fully-supervised benchmarks with just 10% fine-tuning data. Additionally, ViTime exhibits exceptional robustness, handling missing data without imputation and outperforming TimesFM by 20-30% under various data perturbations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new study aims to develop a time series forecasting (TSF) foundation model that can be used across different applications. The researchers propose a special framework called ViTime, which uses a unique combination of techniques to make predictions about future events based on past patterns. The team tested their approach and found that it performed better than other existing methods in many cases. This breakthrough could lead to more accurate forecasting in areas like power generation, transportation, and more. |
Keywords
* Artificial intelligence * Fine tuning * Supervised * Time series * Zero shot
