基于DSN(动态稀疏网络)的时间序列分类（Python完整源码和数据）

# Dynamic Sparse Network for Time Series Classification: Learning What to “See” This is the offical implementation for the paper titled Dynamic Sparse Network for Time Series Classification: Learning What to “See”. ## Requirements - PyTorch 1.4.0 - torchvision 0.2.1 - numpy - pandas ## Dataset - The univariate time series datasets (UCR) could be acquired from [here](http://www.cs.ucr.edu/%7Eeamonn/time_series_data/) - The multivariate time series datasets (UEA) could be acquired from [here](http://www.timeseriesclassification.com/) - Datasets from UCI could be found [here](https://github.com/titu1994/MLSTM-FCN/releases) ## Training 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 ``` ## Other Implementations The implementation is heavily based on [ITOP](https://github.com/Shiweiliuiiiiiii/In-Time-Over-Parameterization) ## Citation ``` @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} } ``` ​ ​