QUANT: A Minimalist Interval Method for Time Series Classification
Machine Learning
2023-08-03 v1
Abstract
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.
Cite
@article{arxiv.2308.00928,
title = {QUANT: A Minimalist Interval Method for Time Series Classification},
author = {Angus Dempster and Daniel F. Schmidt and Geoffrey I. Webb},
journal= {arXiv preprint arXiv:2308.00928},
year = {2023}
}
Comments
26 pages, 20 figures