# iTransformer for Multivariate Time Series Forecasting
This folder contains the reproductions of the iTransformers for Multivariate Time Series Forecasting (MTSF).
## Dataset
Extensive challenging multivariate forecasting tasks are evaluated as the benchmark. We provide the download links: [Google Drive](https://drive.google.com/file/d/1l51QsKvQPcqILT3DwfjCgx8Dsg2rpjot/view?usp=drive_link) or [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/2ea5ca3d621e4e5ba36a/).
To evaluate the model under other input/prediction lengths, feel free to change the ```seq_len``` and ```pred_len``` arguments:
```
# iTransformer on the Electricity Dataset, where 180 time steps are inputted as the observations, and the task is to predict the future 60 steps
python -u run.py \
--is_training 1 \
--root_path ./dataset/electricity/ \
--data_path electricity.csv \
--model_id ECL_180_60 \
--model $model_name \
--data custom \
--features M \
--seq_len 180 \
--pred_len 60 \
--e_layers 3 \
--enc_in 321 \
--dec_in 321 \
--c_out 321 \
--des 'Exp' \
--d_model 512 \
--d_ff 512 \
--batch_size 16 \
--learning_rate 0.0005 \
--itr 1
```
## Training on Custom Dataset
To train with your own time series dataset, you can try out the following steps:
1. Read through the ```Dataset_Custom``` class under the ```data_provider/data_loader``` folder, which provides the functionality to load and process time series files.
2. The file should be ```csv``` format with the first column containing the timestamp and the following columns containing the variates of time series.
3. Set ```data=custom``` and modify the ```enc_in```, ```dec_in```, ```c_out``` arguments according to your number of variates in the training script.
