Time-series analysis is of enormous significance to a multitude of domains such as Internet-of-Things (IoT), prognostics, health, and robotics. Machine learning tasks require time-series data in the form of features for the application of (un)supervised algorithms. The existing feature representation methods lack generality and are domain-specific, especially those based on supervised learning. In this paper, we propose a novel time-series feature representation method based on feature transformation and feature learning. The feature transformation process is inspired by the human cognitive thinking used in visual recognition, where the 1-D time-series data is transformed into a 2-D image dataset. A feature set is learned by imposing a pre-trained convolutional neural network on the transformed search space. This generates two complementary high-dimensional feature sets: (1) one with the matching of the overall 2-D layout of the time-series; and (2) another with matching based on the activation of the local 2-D patterns irrespective of the overall layout. Empirical analysis on a large number of benchmark datasets shows the advantage of the domain-agnostic nature of DeLTa in achieving higher accuracy in comparison to relevant benchmarking methods. Source code is publicly available at https://github.com/technophyte/DeLTa.

时间序列分析对于物联网（IoT），预测，健康和机器人技术等众多领域都具有极其重要的意义。机器学习任务需要采用特征形式的时间序列数据，以应用（无）监督算法。现有的特征表示方法缺乏通用性，并且是特定于领域的，尤其是那些基于监督学习的方法。本文提出了一种基于特征变换和特征学习的时间序列特征表示方法。特征转换过程受到视觉识别中使用的人类认知思维的启发，其中将一维时间序列数据转换为二维图像数据集。通过在变换后的搜索空间上施加预训练的卷积神经网络来学习特征集。这样就产生了两个互补的高维特征集：（1）一个与时间序列的整体二维布局相匹配的特征集；（2）另一个具有基于局部2-D模式激活的匹配，而与整个布局无关。对大量基准数据集的经验分析表明，与相关基准方法相比，DeLTa的领域不可知性具有实现更高准确性的优势。源代码可从https://github.com/technophyte/DeLTa公开获得。对大量基准数据集的经验分析表明，与相关基准方法相比，DeLTa的领域不可知性具有实现更高准确性的优势。源代码可从https://github.com/technophyte/DeLTa公开获得。对大量基准数据集的经验分析表明，与相关基准方法相比，DeLTa的领域不可知性具有实现更高准确性的优势。源代码可从https://github.com/technophyte/DeLTa公开获得。