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 \