A Hybrid Deep Learning Framework for Network Flow Forecasting of Power Grid Enterprise

Algorithm 1

The proposed approach.

(i)	Require: Time series .
(ii)	Ensure: final predictions .
(1)	Use VMD decompose the time series as equation (1) and obtain .
(2)	for to do
(3)	Use GRU-xgboost block improve the quality of input feature vectors and obtain improved reconstructed hidden state ;
(4)	Generate predictions and modelling output of sub-sequence.
(5)	end for
(6)	Aggregate predictions of all sub-sequences and obtain a prediction series .
(7)	Aggregate modelling output of all sub-sequences and calculate a residual series .
(8)	Use residual adjustment block generate compensation values according to the residual series.
(9)	Sum the series and to obtain the final predictions.