Effect of Multi-Scale Decomposition on Performance of Neural Networks in Short-Term Traffic Flow Prediction

Abstract

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. This paper studies the influence of commonly used multi-scale decomposition algorithms including EMD (Empirical Mode Decomposition), EEMD(Ensemble Empirical Mode Decomposition), CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise), VMD (Variational Mode Decomposition), WD (Wavelet Decomposition), and WPD (Wavelet Packet Decomposition) on the performance of Neural Networks. Decomposition algorithms are adopted to decompose traffic flow data into component signals, and then K-means is used to cluster component signals into volatility components, periodic components, and residual components. A Bi-directional LSTM (BiLSTM) neural network is adopted as the standard model for training and forecasting. Finally, three metrics, including prediction performance, robustness, and generalization performance are proposed to evaluate the influence of the multi-scale decomposition algorithm for neural networks comprehensively. By comparing the evaluation results of different hybrid models, this study provides some useful suggestions on proper multi-scale decomposition algorithm selection in short-time traffic flow prediction.

Keywords

• Multi-scale decomposition
• short-term traffic flow
• K-means
• robustness
• generalization performance