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.
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. We thank Haixu Wu and Jiaxiang Dong for insightful discussion and solid support.
If you have difficulties or find bugs in our code, please contact us: