Department of Electronic and Computer Engineering, Inha University, Incheon 22212, Korea.
Sensors (Basel). 2021 Dec 27;22(1):157. doi: 10.3390/s22010157.
Deep neural networks (DNN) have proven to be efficient in computer vision and data classification with an increasing number of successful applications. Time series classification (TSC) has been one of the challenging problems in data mining in the last decade, and significant research has been proposed with various solutions, including algorithm-based approaches as well as machine and deep learning approaches. This paper focuses on combining the two well-known deep learning techniques, namely the Inception module and the Fully Convolutional Network. The proposed method proved to be more efficient than the previous state-of-the-art InceptionTime method. We tested our model on the univariate TSC benchmark (the UCR/UEA archive), which includes 85 time-series datasets, and proved that our network outperforms the InceptionTime in terms of the training time and overall accuracy on the UCR archive.
深度神经网络（DNN）在计算机视觉和数据分类方面已经被证明是高效的，并且有越来越多的成功应用。时间序列分类（TSC）是过去十年数据挖掘中的一个具有挑战性的问题，已经提出了许多不同的解决方案，包括基于算法的方法以及机器和深度学习方法。本文专注于结合两种著名的深度学习技术，即 Inception 模块和全卷积网络。所提出的方法被证明比以前的最先进的 InceptionTime 方法更有效。我们在单变量 TSC 基准（UCR/UEA 档案）上测试了我们的模型，其中包括 85 个时间序列数据集，并证明我们的网络在 UCR 档案的训练时间和整体准确性方面优于 InceptionTime。
