This is the offical implementation for the paper titled Dynamic Sparse Network for Time Series Classification: Learning What to “See”.
- PyTorch 1.4.0
- torchvision 0.2.1
- numpy
- pandas
We truly appreciate everyone who worked on making the datasets available and their contributions to the TSC community.
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The univariate time series datasets (UCR 85 Archive) could be found here
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The multivariate time series datasets (UEA 30 Archive) could be found here
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Datasets from UCI could be found here
To train models for UCR 85 Archive, change the value of --root (e.g., UCR_TS_Archive_2015) and run this command:
python trainer_DSN.py --sparse True --density 0.2 --sparse_init remain_sort --fix False --growth random --depth 4 --ch_size 47 --c_size 3 --k_size 39
To train models for UCI datasets, change the value of --root (e.g., UCI) and run this command:
python trainer_DSN.py --sparse True --density 0.2 --sparse_init remain_sort --fix False --growth random --depth 4 --ch_size 47 --c_size 3 --k_size 39
To train models for UEA 30 Archive, change the value of --root (e.g., UEA_TS_Archive_2018) and run this command:
python trainer_DSN.py --sparse True --density 0.1 --sparse_init remain_sort --fix False --growth random --depth 4 --ch_size 59 --c_size 3 --k_size 39
We appreciate the following github repos a lot for their valuable code.
- https://github.com/Shiweiliuiiiiiii/In-Time-Over-Parameterization
- https://github.com/TimDettmers/sparse_learning
- https://github.com/Wensi-Tang/OS-CNN
- https://github.com/timeseriesAI/tsai
@inproceedings{
xiao2022dynamic,
title={Dynamic Sparse Network for Time Series Classification: Learning What to {\textquotedblleft}See{\textquotedblright}},
author={Qiao Xiao and Boqian Wu and Yu Zhang and Shiwei Liu and Mykola Pechenizkiy and Elena Mocanu and Decebal Constantin Mocanu},
booktitle={Advances in Neural Information Processing Systems},
year={2022},
url={https://openreview.net/forum?id=ZxOO5jfqSYw}
}