  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(N2 * T4) 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 (CNN) 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 1,500 time series in one hour but it can also learn from 8M
time series in 13 hours, a quantity of data that is fully out of reach of
HIVE-COTE.
