Outlier Sub-sequences Detection for Importance Points Segmentation of Time Series
Dajiang Lei · 2012
Because the time series has a large amount of data,detecting it directly will has a high complexity.And this paper proposed an outlier sub-sequences detection algorithm based on importance points segmentation of time series to relieve the problem.Outlier detection of sub-sequences can offset the limitations of the outlier detection of points.This algorithm firstly obtains a series of smoothed important points,and then divides the sequences according to them,meanwhile extracts the four characteristic values of each sub-sequence:length,height,mean and standard deviation,and applies these four characteristic values to the european euclidean distance.Finally,it detects the outlier sub-sequences with the KNN algorithm.Experimental results show that the algorithm is effective and reasonable.