temp cmt

tutorial/dataset_design.md

@@ -1,6 +1,6 @@
 # 📦 Dataset Design
 
-## Data Download
+## ⏬ Data Download
 
 To get started with the datasets, download the `all_data.zip` file from either [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, unzip the files into the `datasets/` directory:
 
@@ -13,7 +13,7 @@ rmdir datasets/all_data
 
 These datasets are preprocessed and ready for immediate use.
 
-## Data Format
+## 💿 Data Format
 
 Each dataset contains at least two essential files: `data.dat` and `desc.json`:
 
@@ -38,7 +38,7 @@ Each dataset contains at least two essential files: `data.dat` and `desc.json`:
         - Evaluation metrics
         - Handling of outliers
 
-## Dataset Class Design
+## 🧑‍💻 Dataset Class Design
 
 <div align="center">
   <img src="figures/DatasetDesign.jpeg" height=250>
@@ -48,10 +48,19 @@ In time series forecasting, datasets are typically generated from raw time serie
 
 BasicTS provides a built-in `Dataset` class called [`TimeSeriesForecastingDataset`](../basicts/data/simple_tsf_dataset.py), designed specifically for time series data. This class generates samples in the form of a dictionary containing two objects: `inputs` and `target`. `inputs` represents the input data, while `target` represents the target data. Detailed documentation can be found in the class's comments.
 
-## How to Add or Customize Datasets
+## 🧑‍🍳 How to Add or Customize Datasets
 
 If your dataset follows the structure described above, you can preprocess your data into the `data.dat` and `desc.json` format and place it in the `datasets/` directory, e.g., `datasets/YOUR_DATA/{data.dat, desc.json}`. BasicTS will then automatically recognize and utilize your dataset.
 
 For reference, you can review the scripts in `scripts/data_preparation/`, which are used to process datasets from `raw_data.zip` ([Google Drive](https://drive.google.com/drive/folders/14EJVODCU48fGK0FkyeVom_9lETh80Yjp?usp=sharing), [Baidu Netdisk](https://pan.baidu.com/s/1shA2scuMdZHlx6pj35Dl7A?pwd=s2xe)).
 
 If your dataset does not conform to the standard format or has specific requirements, you can define your own dataset class by inheriting from `torch.utils.data.Dataset`. In this custom class, the `__getitem__` method should return a dictionary containing `inputs` and `target`.
+
+## Explore Further
+
+- **🧠 [Diving into the Model Convention and Creating Your Own Model](./model.md)**
+- **📉 [Examining the Metrics Convention and Developing Your Own Loss & Metrics](./metrics_design.md)**
+- **🛠️ [Navigating The Scaler Convention and Designing Your Own Scaler](./scaler_design.md)**
+- **🏃‍♂️ [Mastering The Runner Convention and Building Your Own Runner](./runner_design.md)**
+- **📜 [Interpreting the Config File Convention and Customizing Your Configuration](./config_design.md)**
+- **🔍 [Exploring a Variety of Baseline Models](../baselines/)**

tutorial/overall_design.md

@@ -19,3 +19,13 @@ To streamline the configuration of training strategies and centralize all option
 Users can easily configure models, datasets, scaling methods, evaluation metrics, optimizers, learning rates, and other hyperparameters by modifying the configuration file—**as simple as filling out a form**.
 
 For example, setting `CFG.TRAIN.EARLY_STOPPING_PATIENCE = 10` enables early stopping with a patience level of 10.
+
+## Explore Further
+
+- **📦 [Exploring the Dataset Convention and Customizing Your Own Dataset](./dataset_design.md)**
+- **🧠 [Diving into the Model Convention and Creating Your Own Model](./model.md)**
+- **📉 [Examining the Metrics Convention and Developing Your Own Loss & Metrics](./metrics_design.md)**
+- **🛠️ [Navigating The Scaler Convention and Designing Your Own Scaler](./scaler_design.md)**
+- **🏃‍♂️ [Mastering The Runner Convention and Building Your Own Runner](./runner_design.md)**
+- **📜 [Interpreting the Config File Convention and Customizing Your Configuration](./config_design.md)**
+- **🔍 [Exploring a Variety of Baseline Models](../baselines/)**