QUANT: A Minimalist Interval Method for Time Series Classification

TL;DR

QUANT introduces a minimalist interval method for time series classification that achieves state-of-the-art accuracy efficiently by using quantiles, fixed intervals, and a standard classifier, significantly reducing computation time.

Contribution

It demonstrates that a simple, fixed-interval, quantile-based approach can match complex interval methods in accuracy with minimal computational resources.

Findings

Achieves state-of-the-art accuracy on 142 UCR datasets.

Requires less than 15 minutes of total compute time on a single CPU.

Uses only quantiles, fixed intervals, and a standard classifier.

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.