Skip to content

Latest commit

 

History

History
61 lines (53 loc) · 3.54 KB

README.md

File metadata and controls

61 lines (53 loc) · 3.54 KB
Raw

HDC-MiniROCKET

Code to the paper [1]. The approach is based on the time series classification algorithm MiniROCKET [2] and extend it with explicit time encoding by HDC.

[1] Schlegel, K., Neubert, P. & Protzel, P. (2022) HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computing. In Proc. of International Joint Conference on Neural Networks (IJCNN). [2] A. Dempster, D. F. Schmidt, and G. I. Webb, “MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification,” Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 248–257, 2021.

Usage

We recommend using a virtual environment to run the code.

  • Create virtual environment: python3 -m venv venv
  • Activate virtual environment: source venv/bin/activate
  • Install requirements: pip3 install -r requirements.txt

1. Download Dataset:

Dataset download: cd data; wget http://www.timeseriesclassification.com/Downloads/Archives/Univariate2018_ts.zip; unzip Univariate2018_ts.zip; cd ..

2. Run scripts:

File descriptions

  • main.py contains some arguments (dataset, UCR index, scale, HDC dim, etc.)
  • config.py contains more specific parameters for running the experiment
  • main_run.py contains the "trainer" with training and evaluation functions
  • models/HDC_MINIROCKET.py is the backbone of HDC-MiniRocket and contains all necessary functions
  • models/Minirocket_utils is basically the original implementation of MiniROCKET extended by the HDC operations

Arguments to run

  • --dataset parameter as argument of main.py to select between datasets (UCR, synthetic, and synthetic_hard).
  • --complete_UCR parameter as argument of main.py to work with the complete UCR ensemble
  • --multi_scale parameter as argument of main.py to run different scales defined in config.py
  • --scale parameter as argument of main.py to define a specific similarity scale
  • --ensemble_idx parameter as argument of main.py to run a dataset of UCR
  • --config parameter as argument of main.py to specify various configuration parameters defined in config.py
  • --dataset_path parameter as argument of main.py to specify the path to the dataset (default: data/)

Run:

UCR Datasets:

  • run dataset 0 of UCR with scale=0 python3 main.py --model HDC_MINIROCKET --dataset UCR --ensemble_idx 0 --scale 0 --config Config_orig
  • Run the complete UCR Benchmark ensemble with different scales: python3 main.py --model HDC_MINIROCKET --dataset UCR --complete_UCR --multi_scale --config Config_orig
  • run the complete UCR with automatically selecting the best scale (cross validation) python3 main.py --model HDC_MINIROCKET --dataset UCR --complete_UCR --config Config_orig_auto

Synthetic Dataset:

  • run normal synthetic datasets python3 main.py --model HDC_MINIROCKET --dataset synthetic --scale 1 --config Config_orig
  • run hard synthetic datasets python3 main.py --model HDC_MINIROCKET --dataset synthetic_hard --scale 1 --config Config_orig

Time Measurement:

  • run time measurement on HDC MiniROCKET python3 main.py --model HDC_MINIROCKET --dataset UCR --complete_UCR --scale 1 --config Config_time_measure
  • run time measurement on original MiniROCKET python3 main.py --model MINIROCKET --dataset UCR --complete_UCR --config Config_time_measure

Results:

Accuracies:

  • the results will be written and saved in /results in from of Excel spreadsheets and text files

Figures:

  • to plot the figures as in the paper, run the 'plot_figure.m' MATLAB script