High Dimension Time Series Mining Based on State Transition Chain Analysis Method
In concerned with the high dimensions time series, which have the characteristic of multivariable, timevarying and time-lagging, were collected from multistage industrial processes, a method which is used to synchronize high-dimensions time series with temporal and spatial conversion is introduced in this paper. The high-dimensions time series is synchronized in spatial sampling by the conversion method. After the discretization for high-dimensions time series which is preprocessed by synchronization, first the control state is classified into normal state and high risk state by a simple association analysis; then using the method of state transition chain analysis, we successfully find the transition condition when the control system transform normal state into high risk state. This condition can be applied to reduce the quality defects of product and be used to guide the control strategy design of control system.
time series data mining state space state transitionl temporal and spatial conversion
Lv Zhimin Zhang Kai Zhang Xiangwei Zong Shengyue
National Engineering Research Center for Advanced Rolling University of Sci. & Tech. Beijing Beijing, P.R. China, 100083
国际会议
上海
英文
1566-1569
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）