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.Comment: 26 pages, 20 figure