Dynamic Adaptive Piecewise Linear Representation Approach Based on Streaming Time Series. Issue 2 (August 2019)

• Record Type: Journal Article
• Authors: Luo, Wei; Zhan, Peng; Zhang, Qi; Ji, Cun; Zheng, Jiecai; Li, Xueqing
• Is Part Of: Journal of physics. Volume 1302:Issue 2(2019)
• Publication Date: 2019-08
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1302/2/022085
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: With the burgeoning of IoE (Internet of Everything), massive numbers of IoT devices in extensive fields are continuously producing huge number of streaming time series. The high dimensionality and dynamic uncertainty of this kind of data lead to the main challenge on traditional time series data mining research. Accordingly, time series representation methods have been regarded as a necessary pre-processing tool to provide data support for the follow-up time series data mining research. In this paper, we propose a novel dynamic time series representation approach called dynamic adaptive piecewise linear representation (DAPLR) for streaming time series, which can automatically provide a series of piecewise linear representation results to meet the diverse needs of different users.