afcarl / rocket

ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels

ROCKET

Paper

This is the provisional companion repository for Dempster A, Petitjean F, Webb GI (2019) ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels, arXiv:1910.13051.

Most methods for time series classification that attain state-of-the-art accuracy have high computational complexity, requiring significant training time even for smaller datasets, and are intractable for larger datasets. Additionally, many existing methods focus on a single type of feature such as shape or frequency. Building on the recent success of convolutional neural networks for time series classification, we show that simple linear classifiers using random convolutional kernels achieve state-of-the-art accuracy with a fraction of the computational expense of existing methods.

Please cite as:

@article{dempster_etal_2019,
  author  = {Dempster, Angus and Petitjean, Fran\c{c}ois and Webb, Geoffrey I},
  title   = {ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels}
  year    = {2019},
  journal = {arXiv:1910.13051}
}

Results

UCR Archive

• 'Bake off' datasets.
• Additional 2018 datasets.

Scalability

• Number of training examples.
• Time series length.

Requirements

To use ROCKET, you will need:

• Python (3.7+);
• Numba (0.45.1+);
• NumPy; and
• scikit-learn (or equivalent).

All of these should be ready to go in Anaconda.

Basic Use

The key ROCKET functions, generate_kernels(...) and apply_kernels(...), are contained in rocket_functions.py. A worked example is provided in the demo notebook.

Basic use follows this pattern:

# (1) generate random kernels
kernels = generate_kernels(input_length = X_training.shape[1], num_kernels = 10_000)

# (2) transform the training data and train a classifier
X_training_transform = apply_kernels(X = X_training, kernels = kernels)
classifier.fit(X_training_transform, Y_training)

# (3) transform the test data and use the classifier
X_test_transform = apply_kernels(X = X_test, kernels = kernels)
classifier.predict(X_test_transform)

Reproducing the Experiments

reproduce_experiments_bakeoff.py is intended to allow for reproduction of the experiments on the 'bake off' datasets (using the txt versions of the 'bake off' datasets from timeseriesclassification.com).

The required arguments are:

• -i or --input_path, the parent directory for the datasets (probably something like.../Univariate_arff/); and
• -o or --output_path, the directory in which to save the results.

The optional arguments are:

• -n or --num_runs, the number of runs (default 10); and
• -k or --num_kernels, the number of kernels (default 10,000).

As ROCKET is nondeterministic, results will differ between runs. However, any single run should produce representative results.

Examples:

python reproduce_experiments_bakeoff.py -i ./Univariate_arff/ -o ./
python reproduce_experiments_bakeoff.py -i ./Univariate_arff/ -o ./ -n 1 -k 100

Acknowledgements

We thank Professor Eamonn Keogh and all the people who have contributed to the UCR time series classification archive. Figures in our paper showing the ranking of different classifiers and variants of ROCKET were produced using code from Ismail Fawaz et al. (2019).

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