A SERVICE FRAMEWORK FOR LEARNING, QUERYING AND MONITORING MULTIVARIATE TIME SERIES

摘要：

We propose a service framework for Multivariate Time Series Analytics (MTSA) that supports model definition, querying, parameter learning, model evaluation, monitoring, and decision recommendation. Our approach combines the strengths of both domain-knowledge-based and formal-learning-based approaches for maximizing utility over time series. More specifically, we identify multivariate time series parametric estimation problems, in which the objective function is dependent on the time points from which the parameters are learned. We propose an algorithm that guarantees to find the optimal time point(s), and we show that our approach produces results that are superior to those of the domain-knowledge-based approach and the logit regression model. We also develop MTSA data model and query language for the services of parameter learning, querying, and monitoring.

关键词： Service Framework Multivariate Time Series Parameter Learning Decision Support

作者: Chun-Kit Ngan Alexander Brodsky Jessica Lin

作者单位: Department of Computer Science, George Mason University 4400 University Drive MSN 4A5, Fairfax, Virginia 22030-4422, U.S.A.

会议类型: 国际会议

会议名称: 13th International Conference on Enterprise Information System(第13届企业信息系统国际会议 ICEIS 2011)

会议地点: 北京

会议语种:英文

页码: 2561-2570

在线出版日期: 2011-06-08（万方平台首次上网日期，不代表论文的发表时间）