Online Detecting and Predicting Special Patterns over Financial Data Streams

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. During the process, our system presents a pattern matching algorithm to efficiently match possible emerging patterns among data streams, and a probability prediction approach to predict the possible patterns which have not emerged in the system. Experimental results show that these approaches are effective and efficient for online pattern detecting and predicting over thousands of financial data streams.