quant: a minimalist interval method for time series classification

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 distillatio...

Full description

Saved in:
Bibliographic Details
Published inData mining and knowledge discovery Vol. 38; no. 4; pp. 2377 - 2402
Main Authors Dempster, Angus, Schmidt, Daniel F., Webb, Geoffrey I.
Format Journal Article
LanguageEnglish
Published New York Springer US 01.07.2024
Springer Nature B.V
Subjects
Accuracy
Archives & records
Artificial Intelligence
Chemistry and Earth Sciences
Classification
Computer Science
Data Mining and Knowledge Discovery
Datasets
Decision trees
Feature selection
Fourier transforms
Information Storage and Retrieval
Methods
Physics
Statistics for Engineering
Time series
Subseries
Time series classification
Quantile
Fast
Interval
Online AccessGet full text

Cover

More Information
Summary: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 min using a single CPU core.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1384-5810
1573-756X
DOI:10.1007/s10618-024-01036-9