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.

本文将深度学习带入了时间序列分类（TSC）研究的前沿。TSC是机器学习的领域，任务是对时间序列进行分类（或标记）。在该领域的最近几十年的工作已导致分类器准确性的显着进步，而现在，以HIVE-COTE算法为代表的技术水平。虽然非常准确，但由于对于具有N个时间长度T的数据集，训练时间复杂度（\（O（N ^ 2 \ cdot T ^ 4）\）很高，因此HIVE-COTE不能应用于许多现实世界的数据集。例如，从具有短长度\（T = 46 \）时间序列\（N = 1500 \）的小型数据集学习HIVE-COTE需花费8天以上的时间。。同时，深度学习由于其高精度和可扩展性而受到了极大的关注。TSC的深度学习的最新方法可扩展，但准确性不如HIVE-COTE。我们介绍了InceptionTime，这是受Inception-v4体系结构启发的深度卷积神经网络模型的集合。我们的实验表明，InceptionTime在准确性方面可与HIVE-COTE媲美，并且具有更大的可扩展性：它不仅可以在一小时内从1500个时间序列中学习，而且还可以在13小时内从800万个时间序列中学习， HIVE-COTE完全无法访问的数据。