QUANT

QUANT: A Minimalist Interval Method for Time Series Classification

arXiv:2308.00928 (preprint)

We show that it is possible to achieve the same accuracy, on average, as the most accurate existing interval methods for time series classification on a standard set of benchmark datasets using a single type of feature (quantiles), fixed intervals, and an 'off the shelf' classifier. This distillation of interval-based approaches represents a fast and accurate method for time series classification, achieving state-of-the-art accuracy on the expanded set of 142 datasets in the UCR archive with a total compute time (training and inference) of less than 15 minutes using a single CPU core.

Please cite as:

@article{dempster_etal_2023,
  author = {Dempster, Angus and Schmidt, Daniel F and Webb, Geoffrey I},
  title  = {{QUANT}: A Minimalist Interval Method for Time Series Classification},
  year   = {2023},
  note   = {arXiv:2308.00928}
}

Results

UCR Archive (142 Datasets, 30 Resamples)

• Mean Accuracy + Training/Test Times
• Per Resample

Requirements

• Python
• PyTorch
• NumPy
• scikit-learn (or similar)

Code

quant.py

Documentation

Documentation [in progress]

Examples

from quant import Quant
from sklearn.ensemble import ExtraTreesClassifier

[...] # load data -> torch.float32, [num_examples, 1, length]

transform = Quant()

X_training_transform = transform.fit_transform(X_training, Y_training)
X_test_transform = transform.transform(X_test)

classifier = \
ExtraTreesClassifier(
    n_estimators = 200,
    max_features = 0.1,
    criterion = "entropy",
    n_jobs = -1
)
classifier.fit(X_training_transform, Y_training)

predictions = classifier.predict(X_test_transform)

Acknowledgements

We thank Professor Eamonn Keogh and all the people who have contributed to the UCR time series classification archive.

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