A Piecewise Linear Representation Method of Time Series Based on Feature Points

Authors:
Yuelong Zhu;De Wu;Shijin Li
Affiliations:
Hohai University, Nanjing, Jiangsu 210098, China;Hohai University, Nanjing, Jiangsu 210098, China;Hohai University, Nanjing, Jiangsu 210098, China
Venue:
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Year:
2007

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Abstract

In recent years, there has been an explosion of interest in mining time series databases. Representation of the data is the key to efficient and effective solutions. One of the most commonly used representation is piecewise linear approximation, which has been used to support clustering, classification, indexing and association rule mining of time series data. In this paper, we propose a method of piecewise linear representation (PLR) based on feature points. Experiment shows that the method has less fit error to the original time series and has a better ability of adaptation, which can be applied to diverse data environments.