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, time-varying and time-lagging, were collected from multi-stage 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.