Abstract: Online efficient multiple-output Regression is an imperative machine learning procedure for predicting, and
compressing multi-dimensional correlated data streams. This proposed framework presented, a novel online efficient
multiple-output Regression method, called E-MORES, for spilling information. E-MORES can progressively take in the
structure of the Regression coefficients to encourage the models continuous refinement. Taking into account that
constrained expressive capacity of Regression models regularly prompting residual errors being dependent, E-MORES
means to progressively learn and use the structure of the residual errors to enhance the prediction precision. This
framework likewise introduce RandomForest and DecisionTree to predict (classify) the next event type that will happen
during the progress time, that is growing, continuing, shrinking, dissolving, merging or splitting. At long last, this
framework investigates the assembly of EMORES in certain perfect condition. Experiments completed on two
manufactured datasets and, three real world datasets approve the effectiveness and proficiency of E-MORES.
Keywords— RandomForest, DecisionTree, Efficient Multiple-Output Regression Method, Eigen Value