Time Series Piecewise Linear Representation Based on Trend Feature Points

2018 ◽  
pp. 19-28
Author(s):  
Dong-Lin Ma ◽  
Yu-Li Zhang
Keyword(s):  
Time Series ◽  
Piecewise Linear ◽  
Linear Representation ◽  
Feature Points ◽  
Piecewise Linear Representation

A New Method of Time Series Piecewise Linear Representation

2013 ◽  
Vol 705 ◽  
pp. 569-573
Author(s):  
Qiang Wang
Keyword(s):  
Time Series ◽  
Piecewise Linear ◽  
Linear Representation ◽  
New Method ◽  
Time Data ◽  
Hyperbolic Tangent ◽  
Piecewise Linear Representation ◽  
Tangent Function ◽  
Dynamic Growth ◽  
Over Time

To meet requirements of time data dynamic growth , and reflect the different effect to the different segment of time series over time, a new method of piecewise linear representation, called tangent piecewise aggregate approximation (TPAA) is proposed based on hyperbolic tangent function. The method can not only meet requirements of time data dynamic growth, but also reflect time property of the time series. Compared with the existing methods, TPAA method can effectively query time series online.

Time Series Data Mining in Process Industry

2012 ◽  
Vol 532-533 ◽  
pp. 1069-1074
Author(s):  
Jia Ren ◽  
Jin Feng Gao
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Large Scale ◽  
Time Series Data ◽  
Piecewise Linear ◽  
Linear Representation ◽  
Series Data ◽  
Time Series Data Mining ◽  
Piecewise Linear Representation ◽  
Mining Procedure

time series; data mining; knowledge discovery; trend extremum representation. Abstract. In recent years, there has been an explosion of interest in mining time series databases. In this paper, we make some attempts to mining process industrial time series. As with most computer science problems, representation of the data is the key to efficient and effective solutions. We introduce a novel algorithm, Trend Extremum Representation, which is empirically proved to be superior to Piecewise Linear Representation and Important Points Representation in manipulating large-scale industrial data. Then, subsequent mining procedure is undertaken. Through clustering analysis and association rule discovery, several useful rules are derived for differentiating normal and abnormal events in everyday operations.

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