dl_time_series_class

Deep Learning for time series data: A survey and experimental study

Results

UCR Archive

• Raw performance metrics

Requirements

• Python;
• Matplotlib
• Numba;
• NumPy;
• Pandas;
• scikit-learn;
• sktime;
• scipy;
• TensorFlow-GPU;
• tqdm.

Usage

main.py

Arguments:
-d --dataset_names          : dataset names (optional, default=all)
-c --classifier_names       : classifier (optional, default=all)
-o --output_path            : path to results (optional, default=root_dir)
-i --iterations             : number of runs (optional, default=3)
-g --generate_results_csv   : make results.csv (optional, default=False)

Examples:
> python main.py
> python main.py -d Adiac Coffee -c rocket_tf mlp -i 1
> python main.py -g True

The framework expects data from the UCR archive in the .ts format.

The folder structure for the datasets is as follows: /UCRArchive_2018/dataset_name/

For example, the train/test of Adiac should be saved under /UCRArchive_2018/Adiac/

Calling main.py without any arguments trains every model on every dataset.

Results are saved in /results.

To generate a results.csv for the tested models, main.py -g True is called.