A Noise-Immune Boosting Framework for Short-Term Traffic Flow Forecasting

Algorithm 1

The proposed framework for short-term traffic flow forecasting.

Input: V(t), date set of the traffic flow
Output: Split and default directions with max gain;
(1)	Step1: Decompose the data wavelet into high-frequency information cD and low-frequency information cA;
(2)	Step2: Reduce the sampling rate of high-frequency information cD to half to get new high-frequency information l;
(3)	Step3: Decompose the low-frequency information cA and the new high-frequency information l by inverse wavelet to obtain the reconstructed data;
(4)	Step4: Import the reconstructed data into xgboost for training;
(5)
(6)
(7)	for to do
(8)	//enumerate missing value goto right
(9)
(10)	for in sorted( , ascent order by ) do
(11)
(12)
(13)
(14)	end for
(15)	//enumerate missing value goto right
(16)
(17)	for in sorted(, ascent order by ) do
(18)
(19)
(20)
(21)	end for
(22)	end for