Journal of Universal Computer Science
Online detecting special patterns over financial data streams is an interesting and significant work. Existing many algorithms take it as a subsequence similarity matching problem. However, pattern detection on streaming time series is naturally expensive by this means. An efficient segmenting algorithm ONSP (ONline Segmenting and Pruning) is proposed, which is used to find the end points of special patterns. Moreover, a novel metric distance function is introduced which more agrees with human perceptions of pattern similarity.