Title :
The inertia test and trend partition for trend detection in sequential data
Author :
Gao Xuedong; Gu Kan
Author_Institution :
Donlinks School of Economics and Management, University of Science and Technology Beijing, China
fDate :
7/1/2015 12:00:00 AM
Abstract :
This paper focuses on the definition of primitives and the presentation and detection of trends in the field of trend studies. An algorithm using the inertia test to detect trends is also proposed. Experiment results explain the reason why traditional primitives can fit sequential data well, while exposing their limitations at the same time.
Keywords :
"Market research","Fitting","Shape","Partitioning algorithms","Economics","Presses","Testing"
Conference_Titel :
Logistics, Informatics and Service Sciences (LISS), 2015 International Conference on
DOI :
10.1109/LISS.2015.7369685
