Att-SCINet

Requirements

Install the required package first:

cd Att-SCINet
pip install -r requirements.txt

Dataset preparation

All datasets can be downloaded. 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

Run training code

We follow the same settings of SCINet for ETTH1, ETTH2, ETTM1 datasets. The detailed training commands are given as follows.

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
attention	Name of attention module	N/A	None

For ETTH1 dataset:

multivariate, out 24

python run_ETTh.py --data ETTh1 --features M --attention CBAM  \
--seq_len 64 --label_len 24 --pred_len 24 --hidden-size 4 --stacks 1 --levels 3 --lr 3e-3 --batch_size 512 --dropout 0.5 \
--decompose True --train_epochs 200 --patience 10

multivariate, out 48

python run_ETTh.py --data ETTh1 --features M --attention CBAM  \
--seq_len 96 --label_len 48 --pred_len 48 --hidden-size 4 --stacks 1 --levels 3 --lr 0.009 --batch_size 512 --dropout 0.25 \
--decompose True --train_epochs 200 --patience 10

multivariate, out 168

python run_ETTh.py --data ETTh1 --features M --attention CBAM  \
--seq_len 336 --label_len 168 --pred_len 168 --hidden-size 4 --stacks 1 --levels 4 --lr 5e-4 --batch_size 512 --dropout 0.5 \
--decompose True --train_epochs 200 --patience 10

multivariate, out 336

python run_ETTh.py --data ETTh1 --features M --attention CBAM  \
--seq_len 336 --label_len 336 --pred_len 336 --hidden-size 1 --stacks 1 --levels 4 --lr 1e-4 --batch_size 128 --dropout 0.5 \
--decompose True --train_epochs 200 --patience 10

multivariate, out 720

python run_ETTh.py --data ETTh1 --features M --attention CBAM  \
--seq_len 736 --label_len 720 --pred_len 720 --hidden-size 1 --stacks 1 --levels 5 --lr 5e-5 --batch_size 128 --dropout 0.5 \
--decompose True --train_epochs 200 --patience 10

For ETTH2 dataset:

multivariate, out 24

python run_ETTh.py --data ETTh2 --features M --attention CBAM  \
--seq_len 64 --label_len 24 --pred_len 24 --hidden-size 8 --stacks 1 --levels 3 --lr 0.007 --batch_size 512 --dropout 0.25 \
--decompose True --train_epochs 200 --patience 10

multivariate, out 48

python run_ETTh.py --data ETTh2 --features M --attention CBAM  \
--seq_len 192 --label_len 48 --pred_len 48 --hidden-size 4 --stacks 1 --levels 4 --lr 0.007 --batch_size 512 --dropout 0.5 \
--decompose True --train_epochs 200 --patience 10

multivariate, out 168

python run_ETTh.py --data ETTh2 --features M --attention CBAM  \
--seq_len 336 --label_len 168 --pred_len 168 --hidden-size 4 --stacks 1 --levels 4 --lr 5e-5 --batch_size 128 --dropout 0.5 \
--decompose True --train_epochs 200 --patience 10 --RIN True

multivariate, out 336

python run_ETTh.py --data ETTh2 --features M --attention CBAM  \
--seq_len 336 --label_len 336 --pred_len 336 --hidden-size 4 --stacks 1 --levels 4 --lr 5e-5 --batch_size 256 --dropout 0.5 \
--decompose True --train_epochs 200 --patience 10 --RIN True

multivariate, out 720

python run_ETTh.py --data ETTh2 --features M --attention CBAM  \
--seq_len 736 --label_len 720 --pred_len 720 --hidden-size 8 --stacks 1 --levels 5 --lr 1e-5 --batch_size 128 --dropout 0.5 \
--decompose True --train_epochs 200 --patience 10 --RIN True

For ETTM1 dataset:

multivariate, out 24

python run_ETTh.py --data ETTm1 --features M --attention CBAM  \
--seq_len 128 --label_len 24 --pred_len 24 --hidden-size 4 --stacks 1 --levels 3 --lr 0.005 --batch_size 512 --dropout 0.5 \
--decompose True --train_epochs 200 --patience 10

multivariate, out 48

python run_ETTh.py --data ETTm1 --features M --attention CBAM  \
--seq_len 192 --label_len 48 --pred_len 48 --hidden-size 4 --stacks 1 --levels 4 --lr 0.001 --batch_size 512 --dropout 0.5 \
--decompose True --train_epochs 200 --patience 10

multivariate, out 96

python run_ETTh.py --data ETTm1 --features M --attention CBAM  \
--seq_len 384 --label_len 96 --pred_len 96 --hidden-size 4 --stacks 1 --levels 4 --lr 5e-5 --batch_size 256 --dropout 0.5 \
--decompose True --train_epochs 200 --patience 10 --RIN True

multivariate, out 288

python run_ETTh.py --data ETTm1 --features M --attention CBAM  \
--seq_len 672 --label_len 288 --pred_len 288 --hidden-size 4 --stacks 1 --levels 5 --lr 1e-5 --batch_size 512 --dropout 0.5 \
--decompose True --train_epochs 200 --patience 10

multivariate, out 672

python run_ETTh.py --data ETTm1 --features M --attention CBAM  \
--seq_len 672 --label_len 672 --pred_len 672 --hidden-size 4 --stacks 1 --levels 5 --lr 1e-5 --batch_size 128 --dropout 0.5 \
--decompose True --train_epochs 200 --patience 10  --RIN True