Similarity measurement based on key points of time series with different length
Liu Yong-zh · Computer Engineering and Applications Journal · 2014
At present, the similarity of time series is to judge and compare in the raw series, because of the original sequence of high dimension, large amount of calculation,it is not conducive to the similarity comparison. The algorithm of new key points(turning point and extreme point)is presented in this paper, in addition to the key point is found by the extreme method in non-monotonic sequence, it also proposes a new algorithm for monotone sequence of turning points, using this algorithm can compress time series, dimension reduction, and can keep the sequence of contour. The key point(turning point and extreme point)is the most important point characterization of time series, which reflects the sequence of contour. The key point is accurately found out in the sequence,that plays a key role in the time series similarity matching and time series compression. In this paper, the new method of similarity based on key points is proposed, it can calculate the similarity of two sequences, improves the robustness of similar decision, and avoids influence of setting the threshold. The experimental results show that this algorithm can effectively determine the similarity of arbitrary sequences, improves the robustness and reduces human intervention and can help clustering, prediction in data mining.