Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Hassan Ismail Fawaz
- sl:arxiv_num : 1909.04939
- sl:arxiv_published : 2019-09-11T09:32:40Z
- sl:arxiv_summary : Time series classification (TSC) is the area of machine learning interested
in learning how to assign labels to 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 is infeasible to use in many applications because
of its very high training time complexity in O(N^2*T^4) for a dataset with N
time series of length T. For example, it takes HIVE-COTE more than 72,000s to
learn from a small dataset with N=700 time series of short length T=46. Deep
learning, on the other hand, has now received enormous attention because of its
high scalability and state-of-the-art accuracy in computer vision and natural
language processing tasks. Deep learning for TSC has only very recently started
to be explored, with the first few architectures developed over the last 3
years only. The accuracy of deep learning for TSC has been raised to a
competitive level, but has not quite reached the level of HIVE-COTE. This is
what this paper achieves: outperforming HIVE-COTE's accuracy together with
scalability. We take an important step towards finding the AlexNet network for
TSC by presenting InceptionTime---an ensemble of deep Convolutional Neural
Network (CNN) models, inspired by the Inception-v4 architecture. Our
experiments show that InceptionTime slightly outperforms HIVE-COTE with a
win/draw/loss on the UCR archive of 40/6/39. Not only is InceptionTime more
accurate, but it is much faster: InceptionTime learns from that same dataset
with 700 time series in 2,300s but can also learn from a dataset with 8M time
series in 13 hours, a quantity of data that is fully out of reach of HIVE-COTE.@en
- sl:arxiv_title : InceptionTime: Finding AlexNet for Time Series Classification@en
- sl:arxiv_updated : 2019-09-13T14:28:15Z
- sl:bookmarkOf : https://arxiv.org/abs/1909.04939
- sl:creationDate : 2019-09-28
- sl:creationTime : 2019-09-28T10:23:53Z
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