Effect of Multi-Scale Decomposition on Performance of Neural Networks in Short-Term Traffic Flow Prediction
Numerous studies employ multi-scale decomposition to improve the prediction performance of neural networks, but the grounds for selecting the decomposition algorithm are not explained, and the effects of decomposition algorithms on other performance of neural networks are also lacking further study....
Main Authors: | Haichao Huang, Jingya Chen, Xinting Huo, Yufei Qiao, Lei Ma |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9386061/ |
Similar Items
-
Short-term Traffic Flow Prediction Using Artificial Intelligence with Periodic Clustering and Elected Set
by: Erdem Doğan
Published: (2020-02-01) -
Short Term Traffic Flow Prediction of Urban Road Using Time Varying Filtering Based Empirical Mode Decomposition
by: Yanpeng Wang, et al.
Published: (2020-03-01) -
A Hybrid Short-term Traffic Flow Forecasting Method Based on Neural Networks Combined with K-Nearest Neighbor
by: Zhao Liu, et al.
Published: (2018-08-01) -
Short-Term Wind Power Forecasting on Multiple Scales Using VMD Decomposition, K-Means Clustering and LSTM Principal Computing
by: Zexian Sun, et al.
Published: (2019-01-01) -
Short-Term Wind-Speed Forecasting Based on Multiscale Mathematical Morphological Decomposition, K-Means Clustering, and Stacked Denoising Autoencoders
by: Weichao Dong, et al.
Published: (2020-01-01)