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=weather
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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=(0.000913333 0.001379042 0.000607991 0.001470479)
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batches=(32 64 32 128)
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epochs=(60 60 60 60)
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dropouts=(0.4 0.4 0.4 0.4)
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patch_lens=(16 16 16 16)
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lradjs=(type3 type3 type3 type3)
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d_models=(256 128 128 128)
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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=(db3 db3 db3 db2)
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levels=(2 1 2 1)
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tfactors=(3 3 7 7)
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dfactors=(7 7 7 5)
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strides=(8 8 8 8)
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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/weather/ \
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--data_path weather.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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