50 lines
1.2 KiB
Bash
50 lines
1.2 KiB
Bash
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export CUDA_VISIBLE_DEVICES=0
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# Model name
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model_name=WPMixer
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# Datasets and prediction lengths
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dataset=ETTm1
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seq_lens=(512 512 512 512)
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pred_lens=(96 192 336 720)
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learning_rates=(0.001277976 0.002415901 0.001594735 0.002011441)
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batches=(256 256 256 256)
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epochs=(80 80 80 80)
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dropouts=(0.4 0.4 0.4 0.4)
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patch_lens=(48 48 48 48)
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lradjs=(type3 type3 type3 type3)
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d_models=(256 128 256 128)
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patiences=(12 12 12 12)
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# Model params below need to be set in WPMixer.py Line 15, instead of this script
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wavelets=(db2 db3 db5 db5)
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levels=(1 1 1 4)
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tfactors=(5 3 7 3)
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dfactors=(3 7 7 8)
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strides=(24 24 24 24)
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# Loop over datasets and prediction lengths
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for i in "${!pred_lens[@]}"; do
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python -u run.py \
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--is_training 1 \
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--root_path ./data/ETT/ \
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--data_path ETTm1.csv \
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--model_id wpmixer \
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--model $model_name \
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--task_name long_term_forecast \
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--data $dataset \
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--seq_len ${seq_lens[$i]} \
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--pred_len ${pred_lens[$i]} \
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--label_len 0 \
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--d_model ${d_models[$i]} \
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--patch_len ${patch_lens[$i]} \
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--batch_size ${batches[$i]} \
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--learning_rate ${learning_rates[$i]} \
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--lradj ${lradjs[$i]} \
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--dropout ${dropouts[$i]} \
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--patience ${patiences[$i]} \
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--train_epochs ${epochs[$i]} \
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--use_amp
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done
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