A piecewise linear representation method based on importance data points for time series data

With the development of intelligent manufacturing technology, it can be foreseen that time series data generated by smart devices will raise to an unprecedented level. For time series with high amount, high dimension and renewal speed characteristics, resulting in difficult data mining and presentat...

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Bibliographic Details
Published in2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD) pp. 111 - 116
Main Authors Cun Ji, Shijun Liu, Chenglei Yang, Lei Wu, Li Pan, Xiangxu Meng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2016
Subjects
Algorithm design and analysis
Big data
Data mining
Distance measurement
Fitting
fitting error
importance data point
Indexes
piecewise linear representation
Time series analysis
time series data
Online AccessGet full text
DOI10.1109/CSCWD.2016.7565973

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Summary:With the development of intelligent manufacturing technology, it can be foreseen that time series data generated by smart devices will raise to an unprecedented level. For time series with high amount, high dimension and renewal speed characteristics, resulting in difficult data mining and presentation on the original time series data. This paper presented a piecewise linear representation based on importance data points for time series data, which called PLR_IDP for short. The method finds importance data points by calculating the fitting error of single point and piecewise, and then represents time series approximately by linear composed of the importance data points. Results from theoretical analysis and experiments show that PLR_IDP reduces the dimensionality, holds the main characteristic with small fitting error of segments and single points.
DOI:10.1109/CSCWD.2016.7565973