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https://github.com/cure-lab/scinet

The GitHub repository for the paper: “Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction“. (NeurIPS 2022)
https://github.com/cure-lab/scinet

deep-neural-networks forecasting temporal-networks time-series time-series-analysis time-series-forecasting

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The GitHub repository for the paper: “Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction“. (NeurIPS 2022)

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# SCINet

[![Arxiv link](https://img.shields.io/badge/arXiv-Time%20Series%20is%20a%20Special%20Sequence%3A%20Forecasting%20with%20Sample%20Convolution%20and%20Interaction-%23B31B1B)](https://arxiv.org/pdf/2106.09305.pdf)



![state-of-the-art](https://img.shields.io/badge/-STATE--OF--THE--ART-blue?logo=Accenture&labelColor=lightgrey)![pytorch](https://img.shields.io/badge/-PyTorch-%23EE4C2C?logo=PyTorch&labelColor=lightgrey)



This is the original pytorch implementation for the following paper: [SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction](https://arxiv.org/pdf/2106.09305.pdf). Alse see the [Open Review verision](https://openreview.net/pdf?id=AyajSjTAzmg).  



If you find this repository useful for your research work, please consider citing it as follows:



```



@article{liu2022SCINet,



title={SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction},



author={Liu, Minhao and Zeng, Ailing and Chen, Muxi and Xu, Zhijian and Lai, Qiuxia and Ma, Lingna and Xu, Qiang},



journal={Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), 2022},



year={2022}



}



```



## Updates

- [2022-09-15] SCINet has been accepted to NeurIPS 2022!



- [2021-11-10] Added Reversible Instance Normalization RevIN[1] support!



- [2021-09-17] SCINet v1.0 is released



## Features



- [x] Support **11** popular time-series forecasting datasets, namely Electricity Transformer Temperature (ETTh1, ETTh2 and ETTm1) , Traffic, Solar-Energy, Electricity and Exchange Rate and PeMS (PEMS03, PEMS04, PEMS07 and PEMS08), ranging from power, energy, finance and traffic domains.



[comment]: <> (![traffic](https://img.shields.io/badge/🚅-Traffic-yellow))



[comment]: <> (![electric](https://img.shields.io/badge/%F0%9F%92%A1-Electricity-yellow))



[comment]: <> (![Solar Energy](https://img.shields.io/badge/%F0%9F%94%86-Solar%20Energy-yellow))



[comment]: <> (![finance](https://img.shields.io/badge/💵-Finance-yellow))



- [x] Provide all training logs.



- [x] Support RevIN to handle datasets with a large train-test sample distribution gap. To activate, simply add ```--RIN True``` to the command line. [**Read more**](./docs/RevIN.md)



## To-do items



-  Integrate GNN-based spatial models into SCINet for better performance and higher efficiency on spatial-temporal time series. Our preliminary results show that this feature could result in considerable gains on the prediction accuracy of some datasets (e.g., PEMSxx).



-  Generate probalistic forecasting results.



Stay tuned!



## Used Datasets



We conduct the experiments on **11** popular time-series datasets, namely **Electricity Transformer Temperature (ETTh1, ETTh2 and ETTm1) ,  PeMS (PEMS03, PEMS04, PEMS07 and PEMS08) and Traffic, Solar-Energy, Electricity and Exchange Rate**, ranging from **power, energy, finance and traffic domains**. 



### Overall information of the 11 datasets



| Datasets      | Variants | Timesteps | Granularity | Start time | Task Type   |

| ------------- | -------- | --------- | ----------- | ---------- | ----------- |

| ETTh1         | 7        | 17,420    | 1hour       | 7/1/2016   | Multi-step  |

| ETTh2         | 7        | 17,420    | 1hour       | 7/1/2016   | Multi-step  |

| ETTm1         | 7        | 69,680    | 15min       | 7/1/2016   | Multi-step  |

| PEMS03        | 358      | 26,209    | 5min        | 5/1/2012   | Multi-step  |

| PEMS04        | 307      | 16,992    | 5min        | 7/1/2017   | Multi-step  |

| PEMS07        | 883      | 28,224    | 5min        | 5/1/2017   | Multi-step  |

| PEMS08        | 170      | 17,856    | 5min        | 3/1/2012   | Multi-step  |

| Traffic       | 862      | 17,544    | 1hour       | 1/1/2015   | Single-step |

| Solar-Energy  | 137      | 52,560    | 1hour       | 1/1/2006   | Single-step |

| Electricity   | 321      | 26,304    | 1hour       | 1/1/2012   | Single-step |

| Exchange-Rate | 8        | 7,588     | 1hour       | 1/1/1990   | Single-step |



## Get started



### Requirements



Install the required package first:



```

cd SCINet

conda create -n scinet python=3.8

conda activate scinet

pip install -r requirements.txt

```



### Dataset preparation



All datasets can be downloaded [here](https://drive.google.com/drive/folders/1Gv1MXjLo5bLGep4bsqDyaNMI2oQC9GH2?usp=sharing). To prepare all dataset at one time, you can just run:

```

source prepare_data.sh

```

 [![ett](https://img.shields.io/badge/Download-ETT_Dataset-%234285F4?logo=GoogleDrive&labelColor=lightgrey)](https://drive.google.com/drive/folders/1NU85EuopJNkptFroPtQVXMZE70zaBznZ)

[![pems](https://img.shields.io/badge/Download-PeMS_Dataset-%234285F4?logo=GoogleDrive&labelColor=lightgrey)](https://drive.google.com/drive/folders/17fwxGyQ3Qb0TLOalI-Y9wfgTPuXSYgiI)

[![financial](https://img.shields.io/badge/Download-financial_Dataset-%234285F4?logo=GoogleDrive&labelColor=lightgrey)](https://drive.google.com/drive/folders/12ffxwxVAGM_MQiYpIk9aBLQrb2xQupT-) 



The data directory structure is shown as follows. 

```

./

└── datasets/

    ├── ETT-data

    │   ├── ETTh1.csv

    │   ├── ETTh2.csv

    │   └── ETTm1.csv

    ├── financial

    │   ├── electricity.txt

    │   ├── exchange_rate.txt

    │   ├── solar_AL.txt

    │   └── traffic.txt

    └── PEMS

        ├── PEMS03.npz

        ├── PEMS04.npz

        ├── PEMS07.npz

        └── PEMS08.npz

```



### Run training code



We follow the same settings of [StemGNN](https://github.com/microsoft/StemGNN) for PEMS 03, 04, 07, 08 datasets, [MTGNN](https://github.com/nnzhan/MTGNN) for Solar, electricity, traffic, financial datasets, [Informer](https://github.com/zhouhaoyi/Informer2020) for ETTH1, ETTH2, ETTM1 datasets. The detailed training commands are given as follows.



#### For PEMS dataset (All datasets follow Input 12, Output 12):



pems03

```

python run_pems.py --dataset PEMS03 --hidden-size 0.0625 --dropout 0.25 --model_name pems03_h0.0625_dp0.25 --num_decoder_layer 2

```



pems04

```

python run_pems.py --dataset PEMS04 --hidden-size 0.0625 --dropout 0 --model_name pems04_h0.0625_dp0

```



pems07

```

python run_pems.py --dataset PEMS07 --hidden-size 0.03125 --dropout 0.25 --model_name pems07_h0.03125_dp0.25

```



pems08

```

python run_pems.py --dataset PEMS08 --hidden-size 1 --dropout 0.5 --model_name pems08_h1_dp0.5

```



##### PEMS Parameter highlights



| Parameter Name | Description             | Parameter in paper | Default |

| -------------- | ----------------------- | ------------------ | ------- |

| dataset        | Name of dataset         | N/A                | PEMS08  |

| horizon        | Horizon                 | Horizon            | 12      |

| window_size    | Look-back window        | Look-back window   | 12      |

| hidden-size    | hidden expansion        | h                  | 1       |

| levels         | SCINet block levels     | L                  | 2       |

| stacks         | The number of SCINet block| K                | 1       |



#### For Solar dataset:



predict 3 

```

python run_financial.py --dataset_name solar_AL --window_size 160 --horizon 3 --hidden-size 1  --lastWeight 0.5 --stacks 2 --levels 4 --lradj 2 --lr 1e-4 --dropout 0.25 --batch_size 256 --model_name so_I160_o3_lr1e-4_bs256_dp0.25_h1_s2l4_w0.5

```

predict 6

```

python run_financial.py --dataset_name solar_AL --window_size 160 --horizon 6 --hidden-size 0.5 --lastWeight 0.5 --stacks 2 --levels 4 --lradj 2 --lr 1e-4 --dropout 0.25 --batch_size 256 --model_name so_I160_o6_lr1e-4_bs256_dp0.25_h0.5_s2l4_w0.5 

```

predict 12

```

python run_financial.py --dataset_name solar_AL --window_size 160 --horizon 12 --hidden-size 2 --lastWeight 0.5 --stacks 2 --levels 4 --lradj 2 --lr 1e-4 --dropout 0.25 --batch_size 1024 --model_name so_I160_o12_lr1e-4_bs1024_dp0.25_h2_s2l4_w0.5

```

predict 24

```

python run_financial.py --dataset_name solar_AL --window_size 160 --horizon 24 --hidden-size 1 --lastWeight 0.5 --stacks 1 --levels 4 --lradj 2 --lr 1e-4 --dropout 0.25 --batch_size 256 --model_name so_I160_o24_lr1e-4_bs256_dp0.25_h1_s1l4_w0.5

```



#### For Electricity dataset:



predict 3

``` 

python run_financial.py --dataset_name electricity --window_size 168 --horizon 3 --hidden-size 8 --single_step 1 --stacks 2 --levels 3 --lr 9e-3 --dropout 0 --batch_size 32 --model_name ele_I168_o3_lr9e-3_bs32_dp0_h8_s2l3_w0.5 --groups 321 --num_decoder_layer 2

```

predict 6

```

python run_financial.py --dataset_name electricity --window_size 168 --horizon 6 --hidden-size 8 --single_step 1 --stacks 2 --levels 3 --lr 9e-3 --dropout 0 --batch_size 32 --model_name ele_I168_o6_lr9e-3_bs32_dp0_h8_s2l3_w0.5 --groups 321 --num_decoder_layer 3

```

predict 12

```

python run_financial.py --dataset_name electricity --window_size 168 --horizon 12 --hidden-size 8 --single_step 1 --stacks 2 --levels 3 --lr 9e-3 --dropout 0 --batch_size 32 --model_name ele_I168_o12_lr9e-3_bs32_dp0_h8_s2l3_w0.5 --groups 321 --num_decoder_layer 3

```

predict 24

```

python run_financial.py --dataset_name electricity --window_size 168 --horizon 24 --hidden-size 8 --single_step 1 --stacks 2 --levels 3 --lr 9e-3 --dropout 0 --batch_size 32 --model_name ele_I168_o24_lr9e-3_bs32_dp0_h8_s2l3_w0.5 --groups 321 --num_decoder_layer 3

```

predict 96

```

python -u run_financial.py --dataset_name electricity --window_size 96 --horizon 96 --hidden-size 8 --stacks 2 --levels 3 --lr 9e-4 --dropout 0 --batch_size 32 --model_name ele_I96_o96_lr9e-4_bs32_dp0_h8_s2l3_w0.5_n4 --groups 321  --concat_len 0   --normalize 4 --long_term_forecast

```

predict 192

```

python -u run_financial.py --dataset_name electricity --window_size 96 --horizon 192 --hidden-size 8 --stacks 2 --levels 3 --lr 9e-4 --dropout 0 --batch_size 32 --model_name ele_I96_o192_lr9e-4_bs32_dp0_h8_s2l3_w0.5_n4 --groups 321  --concat_len 0   --normalize 4 --long_term_forecast

```

predict 336

```

python -u run_financial.py --dataset_name electricity --window_size 96 --horizon 336 --hidden-size 8 --stacks 2 --levels 3 --lr 9e-4 --dropout 0 --batch_size 32 --model_name ele_I168_o336_lr9e-4_bs32_dp0_h8_s2l3_w0.5_n4 --groups 321  --concat_len 0   --normalize 4 --long_term_forecast

```

predict 720

```

python -u run_financial.py --dataset_name electricity --window_size 96 --horizon 720 --hidden-size 8 --stacks 2 --levels 3 --lr 9e-4 --dropout 0 --batch_size 32 --model_name ele_I168_o24_lr9e-4_bs32_dp0_h8_s2l3_w0.5_n4 --groups 321  --concat_len 0   --normalize 4 --long_term_forecast

```



#### For Traffic dataset (warning: 20,000MiB+ memory usage!):



predict 3 

```

python run_financial.py --dataset_name traffic --window_size 168 --horizon 3 --hidden-size 1 --single_step 1 --stacks 2 --levels 3 --lr 5e-4 --dropout 0.5 --batch_size 16 --model_name traf_I168_o3_lr5e-4_bs16_dp0.5_h1_s2l3_w1.0

```

predict 6

```

python run_financial.py --dataset_name traffic --window_size 168 --horizon 6 --hidden-size 2 --single_step 1 --stacks 1 --levels 3 --lr 5e-4 --dropout 0.25 --batch_size 16 --model_name traf_I168_o6_lr5e-4_bs16_dp0.25_h2_s1l3_w1.0

```

predict 12

```

python run_financial.py --dataset_name traffic --window_size 168 --horizon 12 --hidden-size 0.5 --single_step 1 --stacks 2 --levels 3 --lr 5e-4 --dropout 0.25 --batch_size 16 --model_name traf_I168_o12_lr5e-4_bs16_dp0.25_h0.5_s2l3_w1.0

```

predict 24

```

python run_financial.py --dataset_name traffic --window_size 168 --horizon 24 --hidden-size 2 --single_step 1 --stacks 2 --levels 2 --lr 5e-4 --dropout 0.5 --batch_size 16 --model_name traf_I168_o24_lr5e-4_bs16_dp0.5_h2_s2l2_w1.0

```

predict 96

```

python -u run_financial.py --dataset_name traffic --window_size 96 --horizon 96 --hidden-size 2 --stacks 1 --levels 3 --lr 5e-4 --dropout 0.25 --batch_size 16 --model_name traf_I96_o96_lr5e-4_bs16_dp0.25_h2_s1l3_w1.0 --normalize 4 --long_term_forecast

```

predict 192

```

python -u run_financial.py --dataset_name traffic --window_size 96 --horizon 192 --hidden-size 1 --stacks 1 --levels 3 --lr 5e-4 --dropout 0.25 --batch_size 16 --model_name traf_I96_o192_lr5e-4_bs16_dp0.25_h2_s1l3_w1.0 --normalize 4  --long_term_forecast

```

predict 336

```

python -u run_financial.py --dataset_name traffic --window_size 96 --horizon 336 --hidden-size 1 --stacks 1 --levels 3 --lr 5e-4 --dropout 0.25 --batch_size 16 --model_name traf_I96_o336_lr5e-4_bs16_dp0.25_h2_s1l3_w1.0 --normalize 4 --long_term_forecast

```

predict 720

```

python -u run_financial.py --dataset_name traffic --window_size 96 --horizon 720 --hidden-size 1 --stacks 1 --levels 3 --lr 5e-4 --dropout 0.25 --batch_size 16 --model_name traf_I96_o720_lr5e-4_bs16_dp0.25_h2_s1l3_w1.0 --normalize 4 --long_term_forecast

```

#### For Exchange rate dataset:



predict 3 

```

python run_financial.py --dataset_name exchange_rate --window_size 168 --horizon 3 --hidden-size 0.125 --lastWeight 0.5 --stacks 1 --levels 3 --lr 5e-3 --dropout 0.5 --batch_size 4 --model_name ex_I168_o3_lr5e-3_bs4_dp0.5_h0.125_s1l3_w0.5 --num_decoder_layer 2 --epochs 150

```

predict 6

```

python run_financial.py --dataset_name exchange_rate --window_size 168 --horizon 6 --hidden-size 0.125 --lastWeight 0.5 --stacks 1 --levels 3 --lr 5e-3 --dropout 0.5 --batch_size 4 --model_name ex_I168_o6_lr5e-3_bs4_dp0.5_h0.125_s1l3_w0.5 --num_decoder_layer 2 --epochs 150

```

predict 12

```

python run_financial.py --dataset_name exchange_rate --window_size 168 --horizon 12 --hidden-size 0.125 --lastWeight 0.5 --stacks 1 --levels 3 --lr 5e-3 --dropout 0.5 --batch_size 4 --model_name ex_I168_o12_lr5e-3_bs4_dp0.5_h0.125_s1l3_w0.5 --num_decoder_layer 2 --epochs 150

```

predict 24

```

python run_financial.py --dataset_name exchange_rate --window_size 168 --horizon 24 --hidden-size 0.125 --lastWeight 0.5 --stacks 1 --levels 3 --lr 7e-3 --dropout 0.5 --batch_size 4 --model_name ex_I168_o24_lr7e-3_bs4_dp0.5_h0.125_s1l3_w0.5 --num_decoder_layer 2 --epochs 150

```

predict 96

```

python run_financial.py --dataset_name exchange_rate --epochs 20 --window_size 96 --horizon 96 --hidden-size 0.125 --normalize 3 --lastWeight 0.5 --stacks 1 --levels 3 --lr 5e-5  --dropout 0 --model_name final --num_decoder_layer 2 --long_term_forecast

```

predict 192

```

python run_financial.py --dataset_name exchange_rate --epochs 20 --window_size 96 --horizon 192 --hidden-size 0.125 --normalize 3 --lastWeight 0.5 --stacks 1 --levels 3 --lr 5e-5  --dropout 0 --model_name final --num_decoder_layer 2 --long_term_forecast

```

predict 336

```

python run_financial.py --dataset_name exchange_rate --epochs 20 --window_size 96 --horizon 336 --hidden-size 0.125 --normalize 3 --lastWeight 0.5 --stacks 1 --levels 3 --lr 5e-5  --dropout 0 --model_name final --num_decoder_layer 2 --long_term_forecast

```

predict 720

```

python run_financial.py --dataset_name exchange_rate --epochs 20 --window_size 96 --horizon 720 --hidden-size 0.125 --normalize 3 --lastWeight 0.5 --stacks 1 --levels 3 --lr 5e-5  --dropout 0 --model_name final --num_decoder_layer 2 --long_term_forecast

```



##### Financial Parameter highlights



| Parameter Name | Description               | Parameter in paper      | Default                                |

| -------------- | ------------------------- | ----------------------- | -------------------------------------- |

| dataset_name   | Data name                 | N/A                     | exchange_rate                          |

| horizon        | Horizon                   | Horizon                 | 3                                      |

| window_size    | Look-back window          | Look-back window        | 168                                    |

| batch_size     | Batch size                | batch size              | 8                                      |

| lr             | Learning rate             | learning rate           | 5e-3                                   |

| hidden-size    | hidden expansion          | h                       | 1                                      |

| levels         | SCINet block levels       | L                       | 3                                      |

| stacks         | The number of SCINet block| K                       | 1                                      |

| lastweight     | Loss weight of the last frame| Loss weight ($\lambda$) | 1.0                                 |



#### For ETTH1 dataset:



multivariate, out 24

```

python run_ETTh.py --data ETTh1 --features M  --seq_len 48 --label_len 24 --pred_len 24 --hidden-size 4 --stacks 1 --levels 3 --lr 3e-3 --batch_size 8 --dropout 0.5 --model_name etth1_M_I48_O24_lr3e-3_bs8_dp0.5_h4_s1l3

```

multivariate, out 48

```

python run_ETTh.py --data ETTh1 --features M  --seq_len 96 --label_len 48 --pred_len 48 --hidden-size 4 --stacks 1 --levels 3 --lr 0.009 --batch_size 16 --dropout 0.25 --model_name etth1_M_I96_O48_lr0.009_bs16_dp0.25_h4_s1l3

```

multivariate, out 168

```

python run_ETTh.py --data ETTh1 --features M  --seq_len 336 --label_len 168 --pred_len 168 --hidden-size 4 --stacks 1 --levels 3 --lr 5e-4 --batch_size 32 --dropout 0.5 --model_name etth1_M_I336_O168_lr5e-4_bs32_dp0.5_h4_s1l3

```

multivariate, out 336

```

python run_ETTh.py --data ETTh1 --features M  --seq_len 336 --label_len 336 --pred_len 336 --hidden-size 1 --stacks 1 --levels 4 --lr 1e-4 --batch_size 512 --dropout 0.5 --model_name etth1_M_I336_O336_lr1e-4_bs512_dp0.5_h1_s1l4

```

multivariate, out 720

```

python run_ETTh.py --data ETTh1 --features M  --seq_len 736 --label_len 720 --pred_len 720 --hidden-size 1 --stacks 1 --levels 5 --lr 5e-5 --batch_size 256 --dropout 0.5 --model_name etth1_M_I736_O720_lr5e-5_bs256_dp0.5_h1_s1l5

```

Univariate, out 24

```

python run_ETTh.py --data ETTh1 --features S  --seq_len 64 --label_len 24 --pred_len 24 --hidden-size 8 --stacks 1 --levels 3 --lr 0.007 --batch_size 64 --dropout 0.25 --model_name etth1_S_I64_O24_lr0.007_bs64_dp0.25_h8_s1l3

```

Univariate, out 48

```

python run_ETTh.py --data ETTh1 --features S  --seq_len 720 --label_len 48 --pred_len 48 --hidden-size 4 --stacks 1 --levels 4 --lr 0.0001 --batch_size 8 --dropout 0.5 --model_name etth1_S_I720_O48_lr0.0001_bs8_dp0.5_h4_s1l4

```

Univariate, out 168

```

python run_ETTh.py --data ETTh1 --features S  --seq_len 720 --label_len 168 --pred_len 168 --hidden-size 4 --stacks 1 --levels 4 --lr 5e-5 --batch_size 8 --dropout 0.5 --model_name etth1_S_I720_O168_lr5e-5_bs8_dp0.5_h4_s1l4

```

Univariate, out 336

```

python run_ETTh.py --data ETTh1 --features S  --seq_len 720 --label_len 336 --pred_len 336 --hidden-size 1 --stacks 1 --levels 4 --lr 1e-3 --batch_size 128 --dropout 0.5 --model_name etth1_S_I720_O336_lr1e-3_bs128_dp0.5_h1_s1l4

```

Univariate, out 720

```

python run_ETTh.py --data ETTh1 --features S  --seq_len 736 --label_len 720 --pred_len 720 --hidden-size 4 --stacks 1 --levels 5 --lr 1e-4 --batch_size 32 --dropout 0.5 --model_name etth1_S_I736_O720_lr1e-5_bs32_dp0.5_h4_s1l5

```



#### For ETTH2 dataset:



multivariate, out 24

```

python run_ETTh.py --data ETTh2 --features M  --seq_len 48 --label_len 24 --pred_len 24 --hidden-size 8 --stacks 1 --levels 3 --lr 0.007 --batch_size 16 --dropout 0.25 --model_name etth2_M_I48_O24_lr7e-3_bs16_dp0.25_h8_s1l3

```

multivariate, out 48

```

python run_ETTh.py --data ETTh2 --features M  --seq_len 96 --label_len 48 --pred_len 48 --hidden-size 4 --stacks 1 --levels 4 --lr 0.007 --batch_size 4 --dropout 0.5 --model_name etth2_M_I96_O48_lr7e-3_bs4_dp0.5_h4_s1l4

```

multivariate, out 168

```

python run_ETTh.py --data ETTh2 --features M  --seq_len 336 --label_len 168 --pred_len 168 --hidden-size 0.5 --stacks 1 --levels 4 --lr 5e-5 --batch_size 16 --dropout 0.5 --model_name etth2_M_I336_O168_lr5e-5_bs16_dp0.5_h0.5_s1l4

```

multivariate, out 336

```

python run_ETTh.py --data ETTh2 --features M  --seq_len 336 --label_len 336 --pred_len 336 --hidden-size 1 --stacks 1 --levels 4 --lr 5e-5 --batch_size 128 --dropout 0.5 --model_name etth2_M_I336_O336_lr5e-5_bs128_dp0.5_h1_s1l4

```

multivariate, out 720

```

python run_ETTh.py --data ETTh2 --features M  --seq_len 736 --label_len 720 --pred_len 720 --hidden-size 4 --stacks 1 --levels 5 --lr 1e-5 --batch_size 128 --dropout 0.5 --model_name etth2_M_I736_O720_lr1e-5_bs128_dp0.5_h4_s1l5

```

Univariate, out 24

```

python run_ETTh.py --data ETTh2 --features S  --seq_len 48 --label_len 24 --pred_len 24 --hidden-size 4 --stacks 1 --levels 3 --lr 0.001 --batch_size 16 --dropout 0 --model_name etth2_S_I48_O24_lr1e-3_bs16_dp0_h4_s1l3

```

Univariate, out 48

```

python run_ETTh.py --data ETTh2 --features S  --seq_len 96 --label_len 48 --pred_len 48 --hidden-size 4 --stacks 2 --levels 4 --lr 0.001 --batch_size 32 --dropout 0.5 --model_name etth2_S_I96_O48_lr1e-3_bs32_dp0.5_h4_s2l4

```

Univariate, out 168

```

python run_ETTh.py --data ETTh2 --features S  --seq_len 336 --label_len 168 --pred_len 168 --hidden-size 4 --stacks 1 --levels 3 --lr 1e-4 --batch_size 8 --dropout 0 --model_name etth2_S_I336_O168_lr1e-4_bs8_dp0_h4_s1l3

```

Univariate, out 336

```

python run_ETTh.py --data ETTh2 --features S  --seq_len 336 --label_len 336 --pred_len 336 --hidden-size 8 --stacks 1 --levels 3 --lr 5e-4 --batch_size 512 --dropout 0.5 --model_name etth2_S_I336_O336_lr5e-4_bs512_dp0.5_h8_s1l3

```

Univariate, out 720

```

python run_ETTh.py --data ETTh2 --features S  --seq_len 720 --label_len 720 --pred_len 720 --hidden-size 8 --stacks 1 --levels 3 --lr 1e-5 --batch_size 128 --dropout 0.6 --model_name etth2_S_I736_O720_lr1e-5_bs128_dp0.6_h8_s1l3

```



#### For ETTM1 dataset:



multivariate, out 24

```

python run_ETTh.py --data ETTm1 --features M  --seq_len 48 --label_len 24 --pred_len 24 --hidden-size 4 --stacks 1 --levels 3 --lr 0.005 --batch_size 32 --dropout 0.5 --model_name ettm1_M_I48_O24_lr7e-3_bs16_dp0.25_h8_s1l3

```

multivariate, out 48

```

python run_ETTh.py --data ETTm1 --features M  --seq_len 96 --label_len 48 --pred_len 48 --hidden-size 4 --stacks 2 --levels 4 --lr 0.001 --batch_size 16 --dropout 0.5 --model_name ettm1_M_I96_O48_lr1e-3_bs16_dp0.5_h4_s2l4

```

multivariate, out 96

```

python run_ETTh.py --data ETTm1 --features M  --seq_len 384 --label_len 96 --pred_len 96 --hidden-size 0.5 --stacks 2 --levels 4 --lr 5e-5 --batch_size 32 --dropout 0.5 --model_name ettm1_M_I384_O96_lr5e-5_bs32_dp0.5_h0.5_s2l4

```

multivariate, out 288

```

python run_ETTh.py --data ETTm1 --features M  --seq_len 672 --label_len 288 --pred_len 288 --hidden-size 4 --stacks 1 --levels 5 --lr 1e-5 --batch_size 32 --dropout 0.5 --model_name ettm1_M_I672_O288_lr1e-5_bs32_dp0.5_h0.5_s1l5

```

multivariate, out 672

```

python run_ETTh.py --data ETTm1 --features M  --seq_len 672 --label_len 672 --pred_len 672 --hidden-size 4 --stacks 2 --levels 5 --lr 1e-5 --batch_size 32 --dropout 0.5 --model_name ettm1_M_I672_O672_lr1e-5_bs32_dp0.5_h4_s2l5

```

Univariate, out 24

```

python run_ETTh.py --data ETTm1 --features S  --seq_len 96 --label_len 24 --pred_len 24 --hidden-size 4 --stacks 1 --levels 4 --lr 0.001 --batch_size 8 --dropout 0 --model_name ettm1_S_I96_O24_lr1e-3_bs8_dp0_h4_s1l4

```

Univariate, out 48

```

python run_ETTh.py --data ETTm1 --features S  --seq_len 96 --label_len 48 --pred_len 48 --hidden-size 4 --stacks 1 --levels 3 --lr 0.0005 --batch_size 16 --dropout 0 --model_name ettm1_S_I96_O48_lr5e-4_bs16_dp0_h4_s1l3

```

Univariate, out 96

```

python run_ETTh.py --data ETTm1 --features S  --seq_len 384 --label_len 96 --pred_len 96 --hidden-size 2 --stacks 1 --levels 4 --lr 1e-5 --batch_size 8 --dropout 0 --model_name ettm1_S_I384_O96_lr1e-5_bs8_dp0_h2_s1l4

```

Univariate, out 288

```

python run_ETTh.py --data ETTm1 --features S  --seq_len 384 --label_len 288 --pred_len 288 --hidden-size 4 --stacks 1 --levels 4 --lr 1e-5 --batch_size 64 --dropout 0 --model_name ettm1_S_I384_O288_lr1e-5_bs64_dp0_h4_s1l4

```

Univariate, out 672

```

python run_ETTh.py --data ETTm1 --features S  --seq_len 672 --label_len 672 --pred_len 672 --hidden-size 1 --stacks 1 --levels 5 --lr 1e-4 --batch_size 32 --model_name ettm1_S_I672_O672_lr1e-4_bs32_dp0.5_h1_s1l5

```



##### ETT Parameter highlights



| Parameter Name | Description                  | Parameter in paper | Default                    |

| -------------- | ---------------------------- | ------------------ | -------------------------- |

| root_path      | The root path of subdatasets | N/A                | './datasets/ETT-data/ETT/' |

| data           | Subdataset                   | N/A                | ETTh1                      |

| pred_len       | Horizon                      | Horizon            | 48                         |

| seq_len        | Look-back window             | Look-back window   | 96                         |

| batch_size     | Batch size                   | batch size         | 32                         |

| lr             | Learning rate                | learning rate      | 0.0001                     |

| hidden-size    | hidden expansion             | h                  | 1                          |

| levels         | SCINet block levels          | L                  | 3                          |

| stacks         | The number of SCINet blocks  | K                  | 1                          |



## Special Constraint



- Because of the stacked binary down-sampling method that SCINet adopts, the number of levels (L) and look-back window (W) size should satisfy:)






(The formula might not be shown in the darkmode Github)



## References



[1] [Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift](https://openreview.net/forum?id=cGDAkQo1C0p)



## Contact



If you have any questions, feel free to contact us or post github issues. Pull requests are highly welcomed!



```



Minhao Liu: [email protected]



Ailing Zeng: [email protected]



Zhijian Xu: [email protected]



```



## Acknowledgements



Thank you all for your attention to our work!



This code uses ([Informer](https://github.com/zhouhaoyi/Informer2020), [MTGNN](https://github.com/nnzhan/MTGNN), [StemGNN](https://github.com/microsoft/StemGNN)) as baseline methods for comparison.