CLC number: TP311
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2015-08-10
Cited: 0
Clicked: 7958
Hong Yin, Shu-qiang Yang, Xiao-qian Zhu, Shao-dong Ma, Lu-min Zhang. Symbolic representation based on trend features for knowledge discovery in long time series[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(9): 744-758.
@article{title="Symbolic representation based on trend features for knowledge discovery in long time series",
author="Hong Yin, Shu-qiang Yang, Xiao-qian Zhu, Shao-dong Ma, Lu-min Zhang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="16",
number="9",
pages="744-758",
year="2015",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1400376"
}
%0 Journal Article
%T Symbolic representation based on trend features for knowledge discovery in long time series
%A Hong Yin
%A Shu-qiang Yang
%A Xiao-qian Zhu
%A Shao-dong Ma
%A Lu-min Zhang
%J Frontiers of Information Technology & Electronic Engineering
%V 16
%N 9
%P 744-758
%@ 2095-9184
%D 2015
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1400376
TY - JOUR
T1 - Symbolic representation based on trend features for knowledge discovery in long time series
A1 - Hong Yin
A1 - Shu-qiang Yang
A1 - Xiao-qian Zhu
A1 - Shao-dong Ma
A1 - Lu-min Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 16
IS - 9
SP - 744
EP - 758
%@ 2095-9184
Y1 - 2015
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1400376
Abstract: The symbolic representation of time series has attracted much research interest recently. The high dimensionality typical of the data is challenging, especially as the time series becomes longer. The wide distribution of sensors collecting more and more data exacerbates the problem. Representing a time series effectively is an essential task for decision-making activities such as classification, prediction, and knowledge discovery. In this paper, we propose a new symbolic representation method for long time series based on trend features, called trend feature symbolic approximation (TFSA). The method uses a two-step mechanism to segment long time series rapidly. Unlike some previous symbolic methods, it focuses on retaining most of the trend features and patterns of the original series. A time series is represented by trend symbols, which are also suitable for use in knowledge discovery, such as association rules mining. TFSA provides the lower bounding guarantee. Experimental results show that, compared with some previous methods, it not only has better segmentation efficiency and classification accuracy, but also is applicable for use in knowledge discovery from time series.
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