Title :
A plane regression-based sequence forecast algorithm for stream data
Author :
Zhao, Feng ; Li, Qing-Hua
Author_Institution :
Sch. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China
Abstract :
This paper presents a plane regression-based algorithm, called SFA-PR (sequence forecast algorithm based on plane regression) algorithm, to forecast sequence trends for real-time stream data. After gathering real-time stream data through sliding window, algorithm SFA-PR computes support for appointed sequence and describes plane equation to forecast sequence trends in the future. Comparing with other sequence trends mining algorithms, algorithm SFA-PR can cover much more area and never omit key exceptions.
Keywords :
data mining; regression analysis; sequential estimation; SFA-PR algorithm; data mining; data stream; plane regression algorithm; real-time stream data; sequence forecast algorithm; sequence trends mining algorithm; sliding window; Association rules; Computer science; Data mining; Equations; High performance computing; Knowledge management; Machine learning algorithms; Sequential analysis; Technology forecasting; Technology management; Data stream; plane regression; sequence forecast; sliding window;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527192
