This is the original pytorch implementation for the following paper: SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction. Alse see the Open Review verision.
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}
}
-
[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
- 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.
-
Provide all training logs.
-
Support RevIN to handle datasets with a large train-test sample distribution gap. To activate, simply add
--RIN Trueto the command line. [Read more](./docs/RevIN.md)
-
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!
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.
| 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 |
Install the required package first:
cd SCINet
conda create -n scinet python=3.8
conda activate scinet
pip install -r requirements.txt
All datasets can be downloaded here. To prepare all dataset at one time, you can just run:
source prepare_data.sh
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
We follow the same settings of StemGNN for PEMS 03, 04, 07, 08 datasets, MTGNN for Solar, electricity, traffic, financial datasets, Informer for ETTH1, ETTH2, ETTM1 datasets. The detailed training commands are given as follows.
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
| 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 |
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
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
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
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
| 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 ( |
1.0 |
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
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
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
| 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 |
- 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)
[1] [Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift](https://openreview.net/forum?id=cGDAkQo1C0p)
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]
Thank you all for your attention to our work!
This code uses (Informer, MTGNN, StemGNN) as baseline methods for comparison.