Research Article
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 |
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