docs: ✏️ update docs for v1.0.0

Author: yisongfu

README.md

@@ -42,15 +42,15 @@ $\text{BasicTS}^{+}$ (**Basic** **T**ime **S**eries) is a benchmark library and
 
 ## 📢 Latest Updates
 
-🎉 **Update (Oct 2025)**: BasicTS now has built-in support for [**Selective Learning (NeurIPS'25)**](http://arxiv.org/abs/2510.25207), an effective training strategy to mitigate overfitting and enhance model performance and generalization. Users can import and use it directly from the callback module. [Usage Guide](https://github.com/GestaltCogTeam/selective-learning)
+🎉 **Update (Oct 2025)**: BasicTS now has built-in support for [**Selective Learning (NeurIPS'25)**](http://arxiv.org/abs/2510.25207), an effective training strategy to mitigate overfitting and enhance model performance and generalization. Users can import and use it directly from the [callback module](./src/basicts/runners/callback/selective_learning.py). [Usage Guide](https://github.com/GestaltCogTeam/selective-learning)
 
 🎉 **Update (Oct 2025): BasicTS version 1.0 is released! New Features:**
   - 🚀 **Quick Start with Three Lines of Code**: Install via pip, minimal API design for rapid model training and evaluation.
   - 📦 **Modular Components, Ready to Use**: Provides plug-and-play components like Transformers and MLPs, allowing you to build your own model like building blocks.
-  - 🔄 **Multi-Task Support**: Natively supports three core tasks: time series forecasting, classification, and imputation.
+  - 🔄 **Multi-Task Support**: Natively supports core tasks in time series analysis, including forecasting, classification, and imputation.
   - 🔧 **Highly Extensible Architecture**: Based on Taskflow and Callback mechanisms, enabling easy customization without modifying the Runner.
 
-🎉 **Update (May 2025):** BasicTS now supports training universal forecasting models (e.g., **TimeMoE** and **ChronosBolt**) using the [**BLAST (KDD'24)**](https://arxiv.org/abs/2505.17871) corpus. BLAST enables **faster convergence**, **significantly reduced computational costs**, and achieves superior performance even with limited resources.
+🎉 **Update (May 2025):** BasicTS now supports training universal forecasting models (e.g., **TimeMoE** and **ChronosBolt**) using the [**BLAST (KDD'25)**](https://arxiv.org/abs/2505.17871) corpus. BLAST enables **faster convergence**, **significantly reduced computational costs**, and achieves superior performance even with limited resources.
 
 If you find this project helpful, please don't forget to give it a ⭐ Star to show your support. Thank you!
 

README_CN.md

@@ -43,15 +43,15 @@ $\text{BasicTS}^{+}$ (**Basic** **T**ime **S**eries) 是一个面向时间序列
 
 ## 📢 最新动态
 
-🎉 **更新（2025年10月）**：BasicTS 内置支持[**选择学习(NeurIPS'25)**](http://arxiv.org/abs/2510.25207)，一种有效缓解过拟合，增加模型性能和泛化性的训练策略。用户可以从回调模块中导入并直接使用。[使用说明](https://github.com/GestaltCogTeam/selective-learning)
+🎉 **更新（2025年10月）**：BasicTS 内置支持[**选择学习(NeurIPS'25)**](http://arxiv.org/abs/2510.25207)，一种有效缓解过拟合，增加模型性能和泛化性的训练策略。用户可以从[回调模块](./src/basicts/runners/callback/selective_learning.py)中导入并直接使用。[使用说明](https://github.com/GestaltCogTeam/selective-learning)
 
 🎉 **更新（2025年10月）：BasicTS 1.0版本发布了！新特性：**
   - 🚀 **三行代码，快速上手**​​：pip install 安装，极简 API 设计，快速实现模型训练与评估。
   - 📦 **模块化组件，开箱即用**​​：提供 Transformer、MLP 等即插即用的组件，像搭积木一样构建自己的模型。
-  - 🔄 **多任务支持**​​：原生支持时序预测、分类、插补三大核心任务。
+  - 🔄 **多任务支持**​​：支持时序预测、分类、插补等多个时序分析核心任务。
   - 🔧 **高可扩展架构**​​：基于 Taskflow 与 Callback 机制，无需修改 Runner 即可轻松定制。
 
-🎉 **更新（2025年5月）：** BasicTS 现已支持使用 [**BLAST (KDD'24)**](https://arxiv.org/abs/2505.17871) 语料库训练通用预测模型（例如 **TimeMoE** 和 **ChronosBolt**）。BLAST 能够实现 **更快的收敛速度**、**显著降低计算成本**，并且即使在资源有限的情况下也能获得卓越性能。
+🎉 **更新（2025年5月）：** BasicTS 现已支持使用 [**BLAST (KDD'25)**](https://arxiv.org/abs/2505.17871) 语料库训练通用预测模型（例如 **TimeMoE** 和 **ChronosBolt**）。BLAST 能够实现 **更快的收敛速度**、**显著降低计算成本**，并且即使在资源有限的情况下也能获得卓越性能。
 
 如果你觉得这个项目对你有帮助，别忘了给个⭐Star支持一下，非常感谢！
 

docs/dataset_design.md

@@ -12,7 +12,7 @@ From version 1.0 onward, BasicTS no longer stores data and timestamps in a singl
 
 ## ⏬ Data Download
 
-To start using the built-in datasets, first download the `all_data.zip` file from [Google Drive](https://drive.google.com/drive/folders/14EJVODCU48fGK0FkyeVom_9lETh80Yjp?usp=sharing) or [Baidu Netdisk](https://pan.baidu.com/s/1shA2scuMdZHlx6pj35Dl7A?pwd=s2xe). After downloading, extract the file to the `datasets/` directory:
+To start using the built-in datasets, first download the `all_data.zip` file from [Google Drive](https://drive.google.com/file/d/1m8jh1z4VNMgQ49DRwywyvYYgs3G5WBsB/view?usp=sharing) or [Baidu Netdisk](https://pan.baidu.com/s/1UcZCCKPCeS7mHSnCO4-COA?pwd=j9ev). After downloading, extract the file to the `datasets/` directory:
 
 ```bash
 cd /path/to/project

docs/dataset_design_cn.md

@@ -12,7 +12,7 @@
 
 ## ⏬ 数据下载
 
-要开始使用内置数据集，请先从 [Google Drive](https://drive.google.com/drive/folders/14EJVODCU48fGK0FkyeVom_9lETh80Yjp?usp=sharing) 或 [百度网盘](https://pan.baidu.com/s/1shA2scuMdZHlx6pj35Dl7A?pwd=s2xe) 下载 `all_data.zip` 文件。下载后，将文件解压至 `datasets/` 目录：
+要开始使用内置数据集，请先从 [Google Drive](https://drive.google.com/file/d/1m8jh1z4VNMgQ49DRwywyvYYgs3G5WBsB/view?usp=sharing) or [百度网盘](https://pan.baidu.com/s/1UcZCCKPCeS7mHSnCO4-COA?pwd=j9ev) 下载 `all_data.zip` 文件。下载后，将文件解压至 `datasets/` 目录：
 
 ```bash
 cd /path/to/project