关键词: 时间序列, 关键点, 数据挖掘, 相似性, 不同长度

Abstract: 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.

Key words: time series, key point, data mining, similarity, different length