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scripts/long_term_forecast/ETT_script/WPMixer_ETTm1.sh

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