Radian-distance Based Time Series Similarity Measurement
基于弧度距离的时间序列相似度量

Time series approximation representation and similarity measurement is one of the fundamental tasks in time series data mining, and the key to similarity matching. In view of shortcomings of various existing PLR (Piecewise Linear Representation) based similarity measure approaches, like series-length dependent issue and potential recognition error under multi-resolution, a radian based time series piecewise linear representation and radian-distance based similarity measurement are presented to cater for the rapid online segmentation and similarity calculation in this paper. The proposed method is really simple but intuitive, it retains major shape features of the series by using segment radian for fine grained division, and effectively improves the accuracy and reliability of the measurement under multi-resolution. This method is segmentation algorithm independent and can be further applied to similarity query, pattern matching, classification and clustering for time series.