temp cmt

README.md

@@ -23,16 +23,16 @@ On the other hand, BasicTS provides users with ***easy-to-use and extensible int
 
 We are collecting **TODOs** and **HOWTOs**, if you need more features (*e.g.* more datasets or baselines) or have any questions, please feel free to create an issue or leave a comment [here](https://github.com/zezhishao/BasicTS/issues/95).
 
-If you find this repository useful for your work, please cite it as [such](./citation.bib):
-
-```LaTeX
-@article{shao2023exploring,
-  title={Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis},
-  author={Shao, Zezhi and Wang, Fei and Xu, Yongjun and Wei, Wei and Yu, Chengqing and Zhang, Zhao and Yao, Di and Jin, Guangyin and Cao, Xin and Cong, Gao and others},
-  journal={arXiv preprint arXiv:2310.06119},
-  year={2023}
-}
-```
+> [!IMPORTANT]  
+> If you find this repository useful for your work, please cite it as [such](./citation.bib):
+> ```LaTeX
+> @article{shao2023exploring,
+>    title={Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis},
+>    author={Shao, Zezhi and Wang, Fei and Xu, Yongjun and Wei, Wei and Yu, Chengqing and Zhang, Zhao and Yao, Di and Jin, Guangyin and Cao, Xin and Cong, Gao and others},
+>    journal={arXiv preprint arXiv:2310.06119},
+>    year={2023}
+>  }
+>  ```
 
 ## ✨ Highlighted Features
 

tutorial/getting_started.md

@@ -99,13 +99,13 @@ These datasets have been preprocessed and are ready for use.
 > Other files are optional and may contain additional information, e.g., `adj_mx.pkl` represents a pre-defined prior graph between the time series.
 
 > [!NOTE]  
-> If you are interested in the preprocessing, you can refer to the [preprocessing script](scripts/data_preparation) and `raw_data.zip`.
+> If you are interested in the preprocessing, you can refer to the [preprocessing script](../scripts/data_preparation) and `raw_data.zip`.
 
 ## 🎯 Quick Tutorial: Train & Evaluate Your Model in Three Steps
 
 ### Step 1: Define Your Model
 
-The `forward` function needs to follow the conventions of BasicTS. You can find an example of the Multi-Layer Perceptron (`MLP`) model in [examples/arch.py](examples/arch.py)
+The `forward` function needs to follow the conventions of BasicTS. You can find an example of the Multi-Layer Perceptron (`MLP`) model in [examples/arch.py](../examples/arch.py)
 
 ### Step 2: Define Your Runner
 
@@ -118,10 +118,10 @@ The runner for the `MLP` model can also use this built-in runner.
 
 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, *e.g.*, runner, dataset, scaler, optimizer, loss, and any other hyperparameters.
-An example of the configuration file for the `MLP` model on the `METR-LA` dataset can be found in [examples/regular_config.py](examples/regular_config.py).
+An example of the configuration file for the `MLP` model on the `METR-LA` dataset can be found in [examples/regular_config.py](../examples/regular_config.py).
 
 > [!NOTE]  
-> The configuration file is the core of training and evaluation in BasicTS. [`examples/complete_config.py`](examples/complete_config.py) outlines all the options you can set in the configuration file.
+> The configuration file is the core of training and evaluation in BasicTS. [`examples/complete_config.py`](../examples/complete_config.py) outlines all the options you can set in the configuration file.
 
 Then, you can run the following command to train and evaluate your model: