An efficient multiple-output Regression is an important machine learning technique, this technique is used for
modeling, forecasting, and compressing multi-dimensional data streams. The proposed system consists of an efficient multipleoutput regression method for data streams, called E-MORES. E-MORES have ability to gain the structure of the regression
coefficients that can be used to provide the model’s continuous improvement. E-MORES can dynamically learn the structure of
the residual errors that can be used to improve the prediction accuracy; it also leverages the structure of residual errors to
increase prediction accuracy.
This proposed system also introduces Random Forest, Decision Tree to predict (classify) the next event type that will happen
during the modulation time, that is increasing, continuing, reducing, and splitting and ARIMA model is based on the idea that
the information in the past values of the time series can alone be used to predict future values.
