Multi-step prediction of traffic flow based on wavelet decomposition correlation matrix

Accurate traffic flow forecasting plays an increasingly important role in traffic management and intelligent information service. Mining and analyzing the hidden rules and patterns in the historical data of traffic flow are helpful to understand the rules of the data and better assist the prediction. For the long-term sequence similarity measurement, this paper proposes the correlation matrix sequence description method based on wavelet decomposition, which can better express the sequence information and perform better in the long-term prediction compared with Euclidean distance. Furthermore, we propose a similar search scheme based on the nearest neighbor and seasonality. The searched candidates are input into the prediction model as the attention value, and the output of prediction results is assisted at each step. Compared with the state-of-the-art methods on the PeMS dataset, the proposed model can effectively learn the long-term dependence of time series and perform better in detail, showing advantages in multi-step prediction.