docs: ✏️ update docs for v1.0.0

Author: yisongfu

README.md

@@ -38,19 +38,19 @@
 
 </div>
 
-$\text{BasicTS}^{+}$ (**Basic** **T**ime **S**eries) is a benchmark library and toolkit designed for time series analysis. It now supports a wide range of tasks and datasets such as spatial-temporal forecasting, long-term time series forecasting, classification, and imputation. It covers various types of algorithms such as statistical models, machine learning models, and deep learning models, making it an ideal tool for developing and evaluating time series analysis models. You can find detailed tutorials in [Getting Started](./tutorial/getting_started.md).
+BasicTS (**Basic** **T**ime **S**eries) is a benchmark library and toolkit designed for time series analysis. It now supports a wide range of tasks and datasets such as spatial-temporal forecasting, long-term time series forecasting, classification, and imputation. It covers various types of algorithms such as statistical models, machine learning models, and deep learning models, making it an ideal tool for developing and evaluating time series analysis models. You can find detailed tutorials in [Getting Started](./tutorial/getting_started.md).
 
 ## 📢 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

@@ -39,19 +39,19 @@
 
 </div>
 
-$\text{BasicTS}^{+}$ (**Basic** **T**ime **S**eries) 是一个面向时间序列分析的基准库和工具箱，现已支持时空预测、长序列预测、分类、插补等多种任务与数据集，涵盖统计模型、机器学习模型、深度学习模型等多类算法，为开发和评估时间序列预测模型提供了理想的工具。你可以在[快速上手](./tutorial/getting_started_cn.md)找到详细的教程。
+BasicTS (**Basic** **T**ime **S**eries) 是一个面向时间序列分析的基准库和工具箱，现已支持时空预测、长序列预测、分类、插补等多种任务与数据集，涵盖统计模型、机器学习模型、深度学习模型等多类算法，为开发和评估时间序列预测模型提供了理想的工具。你可以在[快速上手](./tutorial/getting_started_cn.md)找到详细的教程。
 
 ## 📢 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