Electrical and Electronic Engineering |
|
|
|
|
Monitoring correlative financial data streams by local pattern similarity |
Tao JIANG, Yu-cai FENG, Bin ZHANG, Zhong-sheng CAO, Ge FU, Jie SHI |
College of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China; Department of Computer Science, Hengyang Normal University, Hengyang 421008, China |
|
|
Abstract Developing tools for monitoring the correlations among thousands of financial data streams in an online fashion can be interesting and useful work. We aimed to find highly correlative financial data streams in local patterns. A novel distance metric function slope duration distance (SDD) is proposed, which is compatible with the characteristics of actual financial data streams. Moreover, a model monitoring correlations among local patterns (MCALP) is presented, which dramatically decreases the computational cost using an algorithm quickly online segmenting and pruning (QONSP) with O(1) time cost at each time tick t, and our proposed new grid structure. Experimental results showed that MCALP provides an improvement of several orders of magnitude in performance relative to traditional naive linear scan techniques and maintains high precision. Furthermore, the model is incremental, parallelizable, and has a quick response time.
|
Received: 12 June 2008
|
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|