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export CUDA_VISIBLE_DEVICES=7
seq_len=336
python -u long_term_forecast.py \
--task_name "long_term_forecast" \
--dataset_name "ECL" \
--data "custom" \
--root_path "./datasets/electricity/" \
--data_path "electricity.csv" \
--seq_len $seq_len \
--pred_len 96 \
--forward_layers 3 \
--stride 8 \
--batch_size 4 \
--learning_rate 0.0001 \
--lradj "type1" \
--num_workers 5 \
--enc_in 321 \
--train_epochs 16 \
--patience 3 \
python -u long_term_forecast.py \
--task_name "long_term_forecast" \
--dataset_name "ECL" \
--data "custom" \
--root_path "./datasets/electricity/" \
--data_path "electricity.csv" \
--seq_len $seq_len \
--pred_len 192 \
--forward_layers 3 \
--stride 8 \
--batch_size 8 \
--learning_rate 0.00025 \
--lradj "type1" \
--num_workers 5 \
--enc_in 321 \
--train_epochs 16 \
--patience 3 \
python -u long_term_forecast.py \
--task_name "long_term_forecast" \
--dataset_name "ECL" \
--data "custom" \
--root_path "./datasets/electricity/" \
--data_path "electricity.csv" \
--seq_len $seq_len \
--pred_len 336 \
--forward_layers 3 \
--stride 8 \
--batch_size 8 \
--learning_rate 0.00025 \
--lradj "type1" \
--num_workers 5 \
--enc_in 321 \
--train_epochs 16 \
--patience 3 \
python -u long_term_forecast.py \
--task_name "long_term_forecast" \
--dataset_name "ECL" \
--data "custom" \
--root_path "./datasets/electricity/" \
--data_path "electricity.csv" \
--seq_len $seq_len \
--pred_len 720 \
--forward_layers 3 \
--stride 8 \
--batch_size 4 \
--learning_rate 0.0001 \
--lradj "type1" \
--num_workers 5 \
--enc_in 321 \
--train_epochs 16 \
--patience 3 \