Time series classification is a thriving area of research in machine learning. Among many applications, it is frequently applied to human activity analysis. Time series describing a human in motion are ubiquitously collected via omnipresent mobile devices and can be subjected to further processing. In this paper, we propose a novel, deep learning approach to time series classification. It is based on a lagged time series representation stored as images and Convolutional Neural Network used to image classification. We present a comparative study on different variants of lagged time series representation and we evaluate their effectiveness in a series of empirical experiments. We show that the developed method provides satisfying classification accuracy. The proposed image‐based time series encoding is less resource‐consuming than encodings used in other image‐based approaches to time series classification. It is worth to emphasize that the proposed time series encoding conceals original time series values. Images are saved without scales and the order of observations cannot be reconstructed. Thus, the method is particularly suitable for systems that need to store sensitive information.

中文翻译：

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使用卷积神经网络进行基于图像的时间序列分类的滞后编码

时间序列分类是机器学习研究的一个蓬勃发展领域。在许多应用中，它经常应用于人类活动分析。通过无所不在的移动设备无处不在地收集描述运动中的人的时间序列，并且可以对其进行进一步处理。在本文中，我们提出了一种新颖的深度学习时间序列分类方法。它基于存储为图像的滞后时间序列表示和用于图像分类的卷积神经网络。我们提出了对滞后时间序列表示形式的不同变体的比较研究，并在一系列经验实验中评估了它们的有效性。我们表明，所开发的方法提供了令人满意的分类精度。所提出的基于图像的时间序列编码比其他基于图像的时间序列分类方法中使用的编码更少资源消耗。值得强调的是，提出的时间序列编码隐藏了原始时间序列值。图像无比例保存，并且观察顺序无法重建。因此，该方法特别适用于需要存储敏感信息的系统。