Disentangling Domain and General Representations for Time Series Classification

About

This project is the implementation of paper "Disentangling Domain and General Representations for Time Series Classification"

Dependencies

This project is implemented primarily in Python 3.9.16. Some other Python module dependencies are listed in requirements.txt, which can be easily installed with pip:

pip install -r requirements.txt

File folders

dataset: dataset.py for generate dataset.

scripts: some quick starts of ucihar and wisdm dataset.

model: model of CADT

utils: data argumentation of CADT

Run

Before building the project, we recommend switching the working directory to the project root directory. Assume the project root is at <dynamic_triad_root>, then run command

cd <dynamic_triad_root>

Note that we assume <dynamic_triad_root> as your working directory in all the commands presented in the rest of this documentation.Then you can run bash script/ucihar_run.sh to have a quick start. If you need to run CADT on your data, then you will need to modify the following parameters in the script according to the structure of your data.

  --x_dim  9\
  --seq_len 128\
  --source_domain 2\
  --target_domain 4\
  --dataset 'ucihar'

x_dim and seq_len should be your shape of your time series. If your data is already divided into multiple domains, it would be better to include two indicators (source_domain and target_domain) when generating the dataset.