update readme

.gitignore

@@ -0,0 +1,131 @@
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README.md

@@ -29,9 +29,8 @@ We curate large-scale datasets comprised of **1B time points**, proposing a unif
 pip install -r requirements.txt
 ```
 
-1. The datasets can be obtained
-   from [Google Drive](https://drive.google.com/file/d/1yffcQBcMLasQcT7cdotjOVcg-2UKRarw/view?usp=sharing)
-   or [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/93388a1811584564a40a/).
+1. Put the datasets [[Google Drive]](https://drive.google.com/file/d/1yffcQBcMLasQcT7cdotjOVcg-2UKRarw/view?usp=sharing)
+[[Tsinghua Cloud]](https://cloud.tsinghua.edu.cn/f/93388a1811584564a40a/) under the folder ```./dataset/```.
 
 2. Download the pre-trained checkpoints and put them under the folder ```./checkpoints/```.
    * Timer_67M_UTSD_4G [[Google]](https://drive.google.com/file/d/1iTaKjDj7IX-GZZjEv7pKGcgyV7GObj-U/view?usp=sharing) [[Tsinghua]](https://cloud.tsinghua.edu.cn/f/e12e5c08131e481f8df6/)
@@ -130,6 +129,10 @@ The decoder-only architecture provides additional flexibility to accommodate tim
 <img src="./figures/length.png" alt="300" align=center />
 </p>
 
+## Training on Custom Data
+
+Tutorials are provided in this [repo](https://github.com/thuml/iTransformer/tree/main/scripts/multivariate_forecasting).
+
 ## Future Work
 
 We are preparing to release the composition of datasets (UTSD), larger checkpoints, and code for pre-training. Please stay tuned for the update!