An efficient and accurate optimization method of sliding window size for PAA

Author

Jinyang Liu ; Chuanlei Zhang ; Shanwen Zhang ; Weidong Fang

Author_Institution

Sch. of Comput. Sci. & Inf. Eng., Tianjin Univ. of Sci. & Technol., Tianjin, China

fYear

2014

fDate

20-22 Oct. 2014

Firstpage

283

Lastpage

286

Abstract

PAA is an important algorithm in time series dimensionality reduction. However, how to determine the sliding window keeps an open issue for PAA and its derivatives. In this paper, a new optimization method to decide the PAA window is proposed based on root mean square distance measure. A rate of information loss is proposed to overcome the scalability issue, which can be used to balance information loss and query performance improvement caused by PAA transformation. Experiment results with a real time series dataset demonstrate that the method is effective and feasible to determine the PAA window size and optimize the whole performance of PAA algorithm.

Keywords

approximation theory; data mining; mean square error methods; optimisation; time series; PAA algorithm; PAA transformation; PAA window size; information loss; information loss rate; optimization method; performance optimization; piecewise aggregate approximation; query performance improvement; real time series dataset; root mean square distance measure; scalability issue; sliding window size; time series dimensionality reduction; Algorithm design and analysis; Approximation methods; Data mining; Educational institutions; Root mean square; Time measurement; Time series analysis; PAA; dimensionality reduction; root mean square distance; time series data mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Computing, Communications and IT Applications Conference (ComComAp), 2014 IEEE

Conference_Location

Beijing

Print_ISBN

978-1-4799-4813-0

Type

conf

DOI

10.1109/ComComAp.2014.7017211

Filename

7017211