From 0afcee9525f4d547be1704884303c06d428f26ab Mon Sep 17 00:00:00 2001 From: ZX <534098964@qq.com> Date: Thu, 30 Oct 2025 15:59:46 +0800 Subject: [PATCH 1/2] Revisions to TimeCraft --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index bc4a1e9..c5b6d6a 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ ![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. From ccef488f9e08d97bd7d22ed74a0eec4bd1a540a8 Mon Sep 17 00:00:00 2001 From: ZX <534098964@qq.com> Date: Thu, 30 Oct 2025 16:11:07 +0800 Subject: [PATCH 2/2] Fix bugs --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index c5b6d6a..bafbbe2 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,6 @@ ![Logo](./figures/TimeCraft2.png) + https://github.com/user-attachments/assets/a1881005-b072-4657-80d0-813efe7068a5 # Time Series Generation for Real-World Applications