Summary of Flowts: Time Series Generation Via Rectified Flow, by Yang Hu et al.
FlowTS: Time Series Generation via Rectified Flow
by Yang Hu, Xiao Wang, Zezhen Ding, Lirong Wu, Huatian Zhang, Stan Z. Li, Sheng Wang, Jiheng Zhang, Ziyun Li, Tianlong Chen
First submitted to arxiv on: 12 Nov 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 proposes FlowTS, an ODE-based model for time series generation that addresses the computational inefficiencies of diffusion-based models. By learning geodesic paths between distributions using rectified flow with straight-line transport in probability space, FlowTS achieves efficient training and generation while improving performances. The model integrates trend and seasonality decomposition, attention registers, and Rotary Position Embedding (RoPE) to enhance generation authenticity. Experimental results demonstrate state-of-the-art performance on Stock and ETTh datasets for unconditional settings, as well as superior performance in solar forecasting and MuJoCo imputation tasks for conditional settings. FlowTS also enables seamless adaptation from unconditional to conditional generation without retraining, ensuring efficient real-world deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make predictions about future events based on past data. The method is called FlowTS and it’s good at generating accurate forecasts for things like stock prices and weather patterns. One of the key advantages of FlowTS is that it can adapt quickly to changing conditions, which means it can be used in real-world situations without needing a lot of extra training. The results show that FlowTS is better than other methods at making predictions, especially when you’re trying to forecast things like solar energy production or missing data. Overall, the paper presents an innovative approach to time series generation that has important applications. |
Keywords
» Artificial intelligence » Attention » Diffusion » Embedding » Probability » Time series
