The Journal of China Universities of Posts and Telecommunications ›› 2021, Vol. 28 ›› Issue (5): 102-110.doi: 10.19682/j.cnki.1005-8885.2021.0027

• Networks • Previous Articles    

Exo-LSTM: traffic flow prediction based on multifractal wavelet theory


  1. Beijing University of Posts and Telecommunications
  • Received:2020-11-25 Accepted:2021-07-05 Online:2021-10-31 Published:2021-10-29


In order to predict traffic flow more accurately and improve network performance, based on the multifractal wavelet theory, a new traffic prediction model named exo-LSTM is proposed. Exo represents exogenous sequence used to provide a detailed sequence for the model, LSTM represents long short-term memory used to predict unstable traffic flow. Applying multifractal traffic flow to the exo-LSTM model and other existing models, the experiment result proves that exo-LSTM prediction model achieves better prediction accuracy.

Key words:

long-short term memory (LSTM), exogenous sequences, multifractal wavelet model