From 936cded280900d7881eb3d780a9c83403cac4b9c Mon Sep 17 00:00:00 2001
From: ZX <534098964@qq.com>
Date: Fri, 13 Feb 2026 11:54:38 +0800
Subject: [PATCH] update readme

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 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 822cbe2..6155544 100644
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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.
@@ -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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