QUANT

QUANT: A Minimalist Interval Method for Time Series Classification

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)

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.

⚡

YuriWerewolf/quant