Dimension Reduction and Similarity Search for Time Series Based on Regression Coefficient
HUANG Chao1,2, ZHU YangYong1
1.Department of Computing and Information Technology, Fudan University, Shanghai 200433 2.School of Economics and Management, Southeast University, Nanjing 210096
Abstract：Dimension reduction is always necessary when similarity search is conducted in time series. The privious methods are of high time complexity and unintuitive (such as DFT and DWT), or can’t be used for accurate similarity search (such as PAA). This paper brings forward a new dimension reduction method which is called Piecewise Regression Approximation (PRA) for time series based on regression coefficient. The PRA method is of linear time complexity and not sensitive to independent noises. It is proved the similarity search based on PRA method satisfies the lowerbounding lemma, so it is practical and effective. The experiments conducted on reallife datasets validate our conclusions.
黄超，朱扬勇. 基于回归系数的时间序列维约简与相似性查找*[J]. 模式识别与人工智能, 2006, 19(1): 52-57.
HUANG Chao, ZHU YangYong. Dimension Reduction and Similarity Search for Time Series Based on Regression Coefficient. , 2006, 19(1): 52-57.
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2006, Vol. 19 
