research-article

InceptionTime: Finding AlexNet for time series classification

Authors:

Hassan Ismail Fawaz,

Benjamin Lucas,

Germain Forestier,

Charlotte Pelletier,

Daniel F. Schmidt,

Jonathan Weber,

Geoffrey I. Webb,

Lhassane Idoumghar,

Pierre-Alain Muller,

François PetitjeanAuthors Info & Claims

Data Mining and Knowledge Discovery, Volume 34, Issue 6

Pages 1936 - 1962

https://doi.org/10.1007/s10618-020-00710-y

Published: 01 November 2020 Publication History

Abstract

This paper brings deep learning at the forefront of research into time series classification (TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of time series. The last few decades of work in this area have led to significant progress in the accuracy of classifiers, with the state of the art now represented by the HIVE-COTE algorithm. While extremely accurate, HIVE-COTE cannot be applied to many real-world datasets because of its high training time complexity in

O (N^{2} \cdot T^{4})

for a dataset with N time series of length T. For example, it takes HIVE-COTE more than 8 days to learn from a small dataset with

N = 1500

time series of short length

T = 46

. Meanwhile deep learning has received enormous attention because of its high accuracy and scalability. Recent approaches to deep learning for TSC have been scalable, but less accurate than HIVE-COTE. We introduce InceptionTime—an ensemble of deep Convolutional Neural Network models, inspired by the Inception-v4 architecture. Our experiments show that InceptionTime is on par with HIVE-COTE in terms of accuracy while being much more scalable: not only can it learn from 1500 time series in one hour but it can also learn from 8M time series in 13 h, a quantity of data that is fully out of reach of HIVE-COTE.

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cover image Data Mining and Knowledge Discovery

Data Mining and Knowledge Discovery Volume 34, Issue 6

Nov 2020

365 pages

ISSN:1384-5810

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© The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2020.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 November 2020

Accepted: 27 July 2020

Received: 11 September 2019

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