Online Event-Driven Subsequence Matching over Financial Data Sstream

Subsequence similarity matching in time series databases is an im-portant research area for many applications. This paper presents a new approximate approach for automatic online subsequence simi-larity matching over massive data streams. With a simultaneous on-line segmentation and pruning algorithm over the incoming stream, the resulting piecewise linear representation of the data stream fea-tures high sensitivity and accuracy. The similarity denition is based on a permutation followed by a metric distance function, which provides the similarity search with exibility, sensitivity and scalability. Also, the metric-based indexing methods can be applied for speed-up. To reduce the system burden, the event-driven simi-larity search is performed only when there is a potential event. The query sequence is the most recent subsequence of piecewise data representation of the incoming stream which is automatically gen-erated by the system. The retrieved results can be analyzed in dif-ferent ways according to the requirements of specic applications. This paper discusses an application for future data movement pre-diction based on statistical information. Experiments on real stock data are performed. The correctness of trend predictions is used to evaluate the performance of subsequence similarity matching. 1