134 lines
3.0 KiB
Bash
Executable File
134 lines
3.0 KiB
Bash
Executable File
#export CUDA_VISIBLE_DEVICES=0
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model_name=TimeMixer
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seq_len=96
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e_layers=3
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down_sampling_layers=3
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down_sampling_window=2
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learning_rate=0.01
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d_model=16
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d_ff=32
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batch_size=32
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train_epochs=20
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patience=10
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python -u run.py \
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--task_name long_term_forecast \
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--is_training 1 \
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--root_path ./dataset/electricity/ \
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--data_path electricity.csv \
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--model_id ECL_$seq_len'_'96 \
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--model $model_name \
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--data custom \
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--features M \
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--seq_len $seq_len \
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--label_len 0 \
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--pred_len 96 \
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--e_layers $e_layers \
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--d_layers 1 \
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--factor 3 \
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--enc_in 321 \
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--dec_in 321 \
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--c_out 321 \
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--des 'Exp' \
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--itr 1 \
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--d_model $d_model \
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--d_ff $d_ff \
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--batch_size $batch_size \
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--learning_rate $learning_rate \
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--train_epochs $train_epochs \
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--patience $patience \
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--down_sampling_layers $down_sampling_layers \
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--down_sampling_method avg \
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--down_sampling_window $down_sampling_window
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python -u run.py \
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--task_name long_term_forecast \
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--is_training 1 \
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--root_path ./dataset/electricity/ \
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--data_path electricity.csv \
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--model_id ECL_$seq_len'_'192 \
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--model $model_name \
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--data custom \
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--features M \
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--seq_len $seq_len \
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--label_len 0 \
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--pred_len 192 \
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--e_layers $e_layers \
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--d_layers 1 \
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--factor 3 \
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--enc_in 321 \
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--dec_in 321 \
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--c_out 321 \
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--des 'Exp' \
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--itr 1 \
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--d_model $d_model \
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--d_ff $d_ff \
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--batch_size $batch_size \
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--learning_rate $learning_rate \
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--train_epochs $train_epochs \
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--patience $patience \
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--down_sampling_layers $down_sampling_layers \
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--down_sampling_method avg \
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--down_sampling_window $down_sampling_window
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python -u run.py \
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--task_name long_term_forecast \
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--is_training 1 \
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--root_path ./dataset/electricity/ \
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--data_path electricity.csv \
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--model_id ECL_$seq_len'_'336 \
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--model $model_name \
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--data custom \
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--features M \
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--seq_len $seq_len \
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--label_len 0 \
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--pred_len 336 \
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--e_layers $e_layers \
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--d_layers 1 \
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--factor 3 \
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--enc_in 321 \
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--dec_in 321 \
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--c_out 321 \
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--des 'Exp' \
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--itr 1 \
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--d_model $d_model \
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--d_ff $d_ff \
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--batch_size $batch_size \
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--learning_rate $learning_rate \
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--train_epochs $train_epochs \
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--patience $patience \
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--down_sampling_layers $down_sampling_layers \
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--down_sampling_method avg \
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--down_sampling_window $down_sampling_window
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python -u run.py \
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--task_name long_term_forecast \
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--is_training 1 \
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--root_path ./dataset/electricity/ \
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--data_path electricity.csv \
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--model_id ECL_$seq_len'_'720 \
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--model $model_name \
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--data custom \
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--features M \
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--seq_len $seq_len \
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--label_len 0 \
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--pred_len 720 \
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--e_layers $e_layers \
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--d_layers 1 \
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--factor 3 \
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--enc_in 321 \
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--dec_in 321 \
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--c_out 321 \
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--des 'Exp' \
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--itr 1 \
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--d_model $d_model \
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--d_ff $d_ff \
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--batch_size $batch_size \
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--learning_rate $learning_rate \
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--train_epochs $train_epochs \
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--patience $patience \
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--down_sampling_layers $down_sampling_layers \
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--down_sampling_method avg \
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--down_sampling_window $down_sampling_window |