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scripts/short_term_forecast/TimeMixer_M4.sh

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+export CUDA_VISIBLE_DEVICES=0
+
+model_name=TimeMixer
+
+e_layers=4
+down_sampling_layers=1
+down_sampling_window=2
+learning_rate=0.01
+d_model=32
+d_ff=32
+batch_size=16
+
+
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Monthly' \
+  --model_id m4_Monthly \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers $e_layers \
+  --d_layers 1 \
+  --factor 3 \
+  --enc_in 1 \
+  --dec_in 1 \
+  --c_out 1 \
+  --batch_size 128 \
+  --d_model $d_model \
+  --d_ff 32 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate $learning_rate \
+  --train_epochs 50 \
+  --patience 20 \
+  --down_sampling_layers $down_sampling_layers \
+  --down_sampling_method avg \
+  --down_sampling_window $down_sampling_window \
+  --loss 'SMAPE'
+
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Yearly' \
+  --model_id m4_Yearly \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers $e_layers \
+  --d_layers 1 \
+  --factor 3 \
+  --enc_in 1 \
+  --dec_in 1 \
+  --c_out 1 \
+  --batch_size 128 \
+  --d_model $d_model \
+  --d_ff 32 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate $learning_rate \
+  --train_epochs 50 \
+  --patience 20 \
+  --down_sampling_layers $down_sampling_layers \
+  --down_sampling_method avg \
+  --down_sampling_window $down_sampling_window \
+  --loss 'SMAPE'
+
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Quarterly' \
+  --model_id m4_Quarterly \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers $e_layers \
+  --d_layers 1 \
+  --factor 3 \
+  --enc_in 1 \
+  --dec_in 1 \
+  --c_out 1 \
+  --batch_size 128 \
+  --d_model $d_model \
+  --d_ff 64 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate $learning_rate \
+  --train_epochs 50 \
+  --patience 20 \
+  --down_sampling_layers $down_sampling_layers \
+  --down_sampling_method avg \
+  --down_sampling_window $down_sampling_window \
+  --loss 'SMAPE'
+
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Daily' \
+  --model_id m4_Daily \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers $e_layers \
+  --d_layers 1 \
+  --factor 3 \
+  --enc_in 1 \
+  --dec_in 1 \
+  --c_out 1 \
+  --batch_size 128 \
+  --d_model $d_model \
+  --d_ff 16 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate $learning_rate \
+  --train_epochs 50 \
+  --patience 20 \
+  --down_sampling_layers $down_sampling_layers \
+  --down_sampling_method avg \
+  --down_sampling_window $down_sampling_window \
+  --loss 'SMAPE'
+
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Weekly' \
+  --model_id m4_Weekly \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers $e_layers \
+  --d_layers 1 \
+  --factor 3 \
+  --enc_in 1 \
+  --dec_in 1 \
+  --c_out 1 \
+  --batch_size 128 \
+  --d_model $d_model \
+  --d_ff 32 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate $learning_rate \
+  --train_epochs 50 \
+  --patience 20 \
+  --down_sampling_layers $down_sampling_layers \
+  --down_sampling_method avg \
+  --down_sampling_window $down_sampling_window \
+  --loss 'SMAPE'
+
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Hourly' \
+  --model_id m4_Hourly \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers $e_layers \
+  --d_layers 1 \
+  --factor 3 \
+  --enc_in 1 \
+  --dec_in 1 \
+  --c_out 1 \
+  --batch_size 128 \
+  --d_model $d_model \
+  --d_ff 32 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate $learning_rate \
+  --train_epochs 50 \
+  --patience 20 \
+  --down_sampling_layers $down_sampling_layers \
+  --down_sampling_method avg \
+  --down_sampling_window $down_sampling_window \
+  --loss 'SMAPE'