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

Volume

3

fYear

2005

fDate

18-21 Aug. 2005

Firstpage

1559

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on

Conference_Location

Guangzhou, China

Print_ISBN

0-7803-9091-1

Type

conf

DOI

10.1109/ICMLC.2005.1527192

Filename

1527192