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4 changes: 2 additions & 2 deletions README.md
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![Logo](./figures/TimeCraft2.png)

https://github.com/user-attachments/assets/35bc7ee3-f7a2-4949-96fc-1d1b977e0df1
https://github.com/user-attachments/assets/a1881005-b072-4657-80d0-813efe7068a5

# Time Series Generation for Real-World Applications
The rapid advancement of artificial intelligence has increasingly emphasized the critical role of time series data in powering intelligent decision-making across diverse domains, including healthcare, finance, energy, and transportation. In these fields, the ability to generate high-quality synthetic time series has become particularly valuable. **Time series generation** technology plays a vital role in alleviating **data scarcity**, especially in scenarios where collecting real-world data is expensive, time-consuming, or impractical. It also enables **privacy-preserving** analysis by producing realistic but non-identifiable synthetic data, reducing the risks associated with sharing sensitive information. Moreover, it supports **simulation and forecasting in risk-free environments**, allowing researchers and practitioners to safely explore hypothetical scenarios and train robust models. Together, these capabilities make time series generation an essential tool for a wide range of real-world applications.
Expand Down Expand Up @@ -39,7 +39,7 @@ We are excited to announce three major research breakthroughs integrated into **
* **[OATS] Online Data Augmentation for TSFMs:** To empower the next generation of Time Series Foundation Models (TSFMs), we developed **OATS**. It provides a dynamic, online data augmentation engine that synthesizes model-tailored samples during pre-training, significantly improving the generalization and zero-shot performance of large-scale temporal models.
[[Paper]](https://arxiv.org/pdf/2601.19040) | [[Code]](./OATS)

* **[MN-TSG] Continuous Generation with Irregular Observations:** Real-world data is often sparse and non-uniformly sampled. **MN-TSG** enables continuous-time generation by modeling latent physiological or physical dynamics, allowing the synthesis of realistic, high-fidelity temporal patterns even from highly irregular or incomplete observations.
* **[Diff-MN] Continuous Generation with Irregular Observations:** Real-world data is often sparse and non-uniformly sampled. **Diff-MN** enables continuous-time generation by modeling latent physiological or physical dynamics, allowing the synthesis of realistic, high-fidelity temporal patterns even from highly irregular or incomplete observations.
[[Paper]](https://arxiv.org/pdf/2601.13534) | [[Code]](./Diff-MN)

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