QUANT: A Minimalist Interval Method for Time Series Classification
Angus Dempster Daniel F. Schmidt Geoffrey I. Webb
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.