It is often difficult to establish models for accurate time series prediction

as a result of time delay

nonlinearity

and multi-variable coupling characteristics in production processes. A time series prediction method was proposed by applying correlated process variables to time delayed analysis of convolutional neural network (CNN). First

appropriate correlated variables were selected and time series were analyzed between these variables and outputs. Then

results of time series analysis were employed to determine length of temporal time windows of associated variables. Finally

appropriate CNN models were established for time series prediction. Experimental results of a reaction distillation process showed good accuracy in time series prediction of long time frame and large time-delay processes.