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InceptionTime: Finding AlexNet for time series classification

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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Notes

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    https://github.com/hfawaz/InceptionTime.

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Acknowledgements

The authors would like to thank the creators and providers of the datasets. The authors would also like to thank NVIDIA Corporation for the GPU Grant and the Mésocentre of Strasbourg for providing access to the cluster. This work was supported by the ANR TIMES project (Grant ANR-17-CE23-0015) of the French Agence Nationale de la Recherche. François Petitjean is the recipient of an Australian Research Council Discovery Early Career Award (Project Number DE170100037) funded by the Australian Government. This material is based upon work supported by the Air Force Office of Scientific Research, Asian Office of Aerospace Research and Development (AOARD) under award Number FA2386-18-1-4030.

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Correspondence to Hassan Ismail Fawaz.

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Ismail Fawaz, H., Lucas, B., Forestier, G. et al. InceptionTime: Finding AlexNet for time series classification. Data Min Knowl Disc 34, 1936–1962 (2020). https://doi.org/10.1007/s10618-020-00710-y

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Keywords

  • Time series classification
  • Deep learning
  • Scalable model
  • Inception