temp cmt

README.md

@@ -1,7 +1,7 @@
 <div align="center">
   <img src="assets/basicts+_logo_light.png#gh-light-mode-only" height=200>
   <img src="assets/basicts+_logo_dark.png#gh-dark-mode-only" height=200>
-  <h3><b> A Standard and Fair Time Series Forecasting Benchmark and Toolkit. </b></h3>
+  <h3><b> A Fair and Scalable Time Series Forecasting Benchmark and Toolkit. </b></h3>
 </div>
 
 ---
@@ -15,6 +15,22 @@
 
 </div>
 
+<div align="center">
+
+🎉 [**Getting Started**](./tutorial/getting_started.md) **|** 
+💡 [**Overall Design**](./tutorial/overall_design.md)
+
+📦 [**Dataset**](./tutorial/dataset_design.md) **|** 
+🛠️ [**Scaler**](./tutorial/scaler_design.md) **|** 
+🧠 [**Model**](./tutorial/model_design.md) **|** 
+📉 [**Metrics**](./tutorial/metrics_design.md) **|** 
+🏃‍♂️ [**Runner**](./tutorial/runner_design.md) **|** 
+📜 [**Config**](./tutorial/config_design.md.md) **|** 
+📜 [**Baselines**](./baselines/)
+
+</div>
+
+
 $\text{BasicTS}^{+}$ (**Basic** **T**ime **S**eries **P**lus) is an enhanced benchmark and toolbox designed for time series forecasting. $\text{BasicTS}^{+}$ evolved from its predecessor, [BasicTS](https://github.com/zezhishao/BasicTS/blob/v1/README.md), and now has robust support for spatial-temporal forecasting and long time-series forecasting as well as more general tasks. For brevity and consistency, we will interchangeably refer to this project as $\text{BasicTS}^{+}$ and $\text{BasicTS}$.
 
 On the one hand, BasicTS utilizes a ***unified and standard pipeline*** to give a ***fair and exhaustive*** reproduction and comparison of popular deep learning-based models. 
@@ -81,108 +97,14 @@ BasicTS implements a wealth of models, including classic models, spatial-tempora
 - DCRNN, Graph WaveNet, MTGNN, STID, D2STGNN, STEP, DGCRN, DGCRN, STNorm, AGCRN, GTS, StemGNN, MegaCRN, STGCN, STWave, STAEformer, GMSDR, ...
 - Informer, Autoformer, FEDformer, Pyraformer, DLinear, NLinear, Triformer, Crossformer, ...
 
-## 💿 Dependencies
-
-<details>
-  <summary><b>Preliminaries</b></summary>
-
-
-### OS
-
-We recommend using BasicTS on Linux systems (*e.g.* Ubuntu and CentOS). 
-Other systems (*e.g.*, Windows and macOS) have not been tested.
-
-### Python
-
-Python >= 3.6 (recommended >= 3.9).
-
-[Miniconda](https://docs.conda.io/en/latest/miniconda.html) or [Anaconda](https://www.anaconda.com/) are recommended to create a virtual python environment.
-
-### Other Dependencies
-</details>
-
-BasicTS is built based on PyTorch and [EasyTorch](https://github.com/cnstark/easytorch).
-You can install PyTorch following the instruction in [PyTorch](https://pytorch.org/get-started/locally/). For example:
-
-```bash
-pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html
-```
-
-**After ensuring** that PyTorch is installed correctly, you can install other dependencies via:
-
-```bash
-pip install -r requirements.txt
-```
-
-### Warning
-
-BasicTS is built on PyTorch 1.9.1 or 1.10.0, while other versions have not been tested.
-
-
-## 🎯 Getting Started of Developing with BasicTS
-
-### Preparing Data
-
-- **Clone BasicTS**
+## 🚀 Installation and Quick Start
 
-    ```bash
-    cd /path/to/your/project
-    git clone https://github.com/zezhishao/BasicTS.git
-    ```
+For detailed instructions, please refer to the [Getting Started](./tutorial/getting_started.md) tutorial.
 
-- **Download Raw Data**
-
-    You can download all the raw datasets at [Google Drive](https://drive.google.com/drive/folders/14EJVODCU48fGK0FkyeVom_9lETh80Yjp) or [Baidu Yun](https://pan.baidu.com/s/10gOPtlC9M4BEjx89VD1Vbw)(password: 6v0a), and unzip them to `datasets/raw_data/`.
-
-- **Pre-process Data**
-
-    ```bash
-    cd /path/to/your/project
-    python scripts/data_preparation/${DATASET_NAME}/generate_training_data.py
-    ```
-
-    Replace `${DATASET_NAME}` with one of `METR-LA`, `PEMS-BAY`, `PEMS03`, `PEMS04`, `PEMS07`, `PEMS08`, or any other supported dataset. The processed data will be placed in `datasets/${DATASET_NAME}`.
-
-
-### 3 Steps to Evaluate Your Model
-
-- **Define Your Model Architecture**
-
-    The `forward` function needs to follow the conventions of BasicTS. You can find an example of the Multi-Layer Perceptron (`MLP`) model in [baselines/MLP/mlp_arch.py](baselines/MLP/mlp_arch.py)
-
-- **Define Your Runner for Your Model** (Optional)
-
-    BasicTS provides a unified and standard pipeline in `basicts.runner.BaseTimeSeriesForecastingRunner`.
-    Nevertheless, you still need to define the specific forward process (the `forward` function in the **runner**).
-    Fortunately, BasicTS also provides such an implementation in `basicts.runner.SimpleTimeSeriesForecastingRunner`, which can cover most of the situations.
-    The runner for the `MLP` model can also use this built-in runner.
-    You can also find more runners in `basicts.runners.runner_zoo` to learn more about the runner design.
-
-- **Configure your Configuration File**
-
-    You can configure all the details of the pipeline and hyperparameters in a configuration file, *i.e.*, **everything is based on config**.
-    The configuration file is a `.py` file, in which you can import your model and runner and set all the options. BasicTS uses `EasyDict` to serve as a parameter container, which is extensible and flexible to use.
-    An example of the configuration file for the `MLP` model on the `METR-LA` dataset can be found in [baselines/MLP/MLP_METR-LA.py](baselines/MLP/MLP_METR-LA.py)
-
-### Run It!
-
-- **Reproducing Built-in Models**
-
-  BasicTS provides a wealth of built-in models. You can reproduce these models by running the following command:
-
-  ```bash
-  python experiments/train.py -c baselines/${MODEL_NAME}/${DATASET_NAME}.py --gpus '0'
-  ```
-
-  Replace `${DATASET_NAME}` and `${MODEL_NAME}` with any supported models and datasets. For example, you can run Graph WaveNet on METR-LA dataset by:
-
-  ```bash
-  python experiments/train.py -c baselines/GWNet/METR-LA.py --gpus '0'
-  ```
-
-- **Customized Your Own Model**
+## 📉 Main Results
 
-  [Example: Multi-Layer Perceptron (MLP)](baselines/MLP)
+See the paper *[Exploring Progress in Multivariate Time Series Forecasting:
+Comprehensive Benchmarking and Heterogeneity Analysis](https://arxiv.org/pdf/2310.06119.pdf).*
 
 ## Contributors ✨
 
@@ -212,11 +134,6 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
 
 This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!
 
-## 📉 Main Results
-
-See the paper *[Exploring Progress in Multivariate Time Series Forecasting:
-Comprehensive Benchmarking and Heterogeneity Analysis](https://arxiv.org/pdf/2310.06119.pdf).*
-
 ## 🔗 Acknowledgement
 
 BasicTS is developed based on [EasyTorch](https://github.com/cnstark/easytorch), an easy-to-use and powerful open-source neural network training framework.