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scripts/long_term_forecast/AugmentSample/Classification/PatchTST.sh

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+export CUDA_VISIBLE_DEVICES=0
+
+model_name=PatchTST
+
+for aug in jitter scaling permutation magwarp timewarp windowslice windowwarp rotation spawner dtwwarp shapedtwwarp wdba discdtw discsdtw
+do
+echo using augmentation: ${aug}
+
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/EthanolConcentration/ \
+  --model_id EthanolConcentration \
+  --model $model_name \
+  --data UEA \
+  --e_layers 3 \
+  --batch_size 16 \
+  --d_model 128 \
+  --d_ff 256 \
+  --top_k 3 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 100 \
+  --patience 10 \
+  --augmentation_ratio 1 \
+  --${aug}
+ done

scripts/long_term_forecast/AugmentSample/Forecasting/PatchTST.sh

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+export CUDA_VISIBLE_DEVICES=0
+
+model_name=PatchTST
+for aug in jitter scaling permutation magwarp timewarp windowslice windowwarp rotation spawner dtwwarp shapedtwwarp discdtw discsdtw
+do
+for pred_len in 96 192 336 720
+do
+echo using augmentation: ${aug}
+
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/exchange_rate/ \
+  --data_path exchange_rate.csv \
+  --model_id Exchange_96_${pred_len} \
+  --model $model_name \
+  --data custom \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len ${pred_len} \
+  --e_layers 2 \
+  --d_layers 1 \
+  --factor 3 \
+  --enc_in 8 \
+  --dec_in 8 \
+  --c_out 8 \
+  --des 'Exp' \
+  --itr 1 \
+  --augmentation_ratio 1 \
+  --${aug}
+done
+done

scripts/long_term_forecast/AugmentSample/ReadMe.md

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+# Augmentation Feature Roadbook
+
+Hi there! For those who are interested in testing 
+augmentation techniques in `Time-Series-Library`.
+
+For now, we have embedded several augmentation methods
+in this repo. We are still collecting publicly available 
+augmentation algorithms, and we appreciate your valuable
+advice!
+
+```
+The Implemented Augmentation Methods
+1. jitter 
+2. scaling 
+3. permutation 
+4. magwarp 
+5. timewarp 
+6. windowslice 
+7. windowwarp 
+8. rotation 
+9. spawner 
+10. dtwwarp 
+11. shapedtwwarp 
+12. wdba (Specially Designed for Classification tasks)
+13. discdtw
+```
+
+## Usage
+
+In this folder, we present two sample of shell scripts 
+doing augmentation in `Forecasting` and `Classification`
+tasks.
+
+Take `Forecasting` task for example, we test multiple
+augmentation algorithms on `EthanolConcentration` dataset
+(a subset of the popular classification benchmark `UEA`) 
+using `PatchTST` model.
+
+```shell
+export CUDA_VISIBLE_DEVICES=0
+
+model_name=PatchTST
+
+for aug in jitter scaling permutation magwarp timewarp windowslice windowwarp rotation spawner dtwwarp shapedtwwarp wdba discdtw discsdtw
+do
+echo using augmentation: ${aug}
+
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/EthanolConcentration/ \
+  --model_id EthanolConcentration \
+  --model $model_name \
+  --data UEA \
+  --e_layers 3 \
+  --batch_size 16 \
+  --d_model 128 \
+  --d_ff 256 \
+  --top_k 3 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 100 \
+  --patience 10 \
+  --augmentation_ratio 1 \
+  --${aug}
+ done
+```
+
+Here, parameter `augmentation_ratio` represents how many
+times do we want to perform our augmentation method.
+Parameter `${aug}` represents a string of augmentation
+type label. 
+
+The example here only perform augmentation once, so we
+can set `augmentation_ratio` to `1`, followed by one
+augmentation type label. Trivially, you can set 
+`augmentation_ratio` to an integer `num` followed by 
+`num` augmentation type labels.
+
+The augmentation code obeys the same prototype of 
+`Time-Series-Library`. If you want to adjust other 
+training parameters, feel free to add arguments to the
+shell scripts and play around. The full list of parameters
+can be seen in `run.py`.
+
+## Contact Us!
+
+This piece of code is written and maintained by 
+[Yunzhong Qiu](https://github.com/DigitalLifeYZQiu). 
+We thank [Haixu Wu](https://github.com/wuhaixu2016) and
+[Jiaxiang Dong](https://github.com/dongjiaxiang) for 
+insightful discussion and solid support.
+
+If you have difficulties or find bugs in our code, please
+contact us:
+- Email: qiuyz24@mails.tsinghua.edu.cn