DeLTa: Deep local pattern representation for time series clustering and classification using visual perception
Abstract 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
Knowl. Based Syst.