QUANT: A Minimalist Interval Method for Time Series Classification

Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
8/2/2023
cs.LG

No official code found. Best implementation: angus924/quant (24 stars, GPL-3.0, last commit 2025-03-11).

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.

View on arXivView PDF

Code Implementations(1)

angus924/quantCommunity100%
GPL-3.0

QUANT: A Minimalist Interval Method for Time Series Classification

243PythonJul 31, 20231 years agoGPL-3.0
intervalscalabletime-series-classification

Similar Papers

Back to Basics: A Sanity Check on Modern Time Series Classification Algorithms

85% match1 repo32023

Fast Classification of Large Time Series Datasets

82% match2023

MIHT: A Hoeffding Tree for Time Series Classification using Multiple Instance Learning

80% match2026

TEX-MEX: Similarity-Guided Temporal Contrastive Learning for Time Series

79% match2026

Explaining Time Series Classifiers with PHAR: Rule Extraction and Fusion from Post-hoc Attributions

79% match2025

Unsupervised Representation Learning for Time Series: A Review

79% match1 repo512023

Cite this paper

@article{dempster2023quant,
  title  = {QUANT: A Minimalist Interval Method for Time Series Classification},
  author = {Angus Dempster and Daniel F. Schmidt and Geoffrey I. Webb},
  year   = {2023},
  eprint = {2308.00928},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.00928v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2308.00928.svg)](https://codeofpaper.com/paper/2308.00928)

Discussion