Time series analysis based on enhanced NLCS

Similarity analysis plays a key role in clustering of time series. Normalized longest common subsequence (NLCS) is a similarity measurement widely used in comparing character sequences. In this paper, we developed the NLCS and present a novel algorithm to precisely calculate the similarity of time series. The algorithm used the sum of all common subsequence instead of longest common subsequence which can not represent the similarity of sequences accurately. The experiments based on synthetic and real-life datasets shown that the proposed algorithm performed better in comparing the similarity of time series. Comparing with Euclidean distance on four cluster validity indices, the results lead to a better performance by k-means or self-organize map.