Open AccessDynamic Adaptive Piecewise Linear Representation Approach Based on Streaming Time Series
Author(s) -
Wei Luo,
Peng Zhan,
Qi Zhang,
Cun Ji,
Jiecai Zheng,
Xueqing Li
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1302/2/022085
Subject(s) - representation (politics) , computer science , series (stratigraphy) , time series , curse of dimensionality , dynamic data , piecewise , piecewise linear function , streaming data , data mining , external data representation , theoretical computer science , algorithm , artificial intelligence , machine learning , mathematics , database , paleontology , mathematical analysis , geometry , politics , political science , law , biology
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.