QUANT: A Minimalist Interval Method for Time Series Classification¶
Authors: Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
Published: 2023 ()
Algorithm: QUANT
arXiv: 2308.00928
Summary¶
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.