Ceemdan-ipso-lstm: a novel model for short-term passenger flow prediction in urban rail transit systems

HIGHLIGHTS

• who: Lu Zeng et al. from the School of Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou, China have published the research: CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems, in the Journal: (JOURNAL) of 22/09/2020
• what: The authors use the CEEMDAN algorithm to break down the time series data for the passenger flow, use the LSTM hyperparameters as the object of optimization, combine them with the IPSO algorithm to determine the optimal value of the LSTM hyperparameters, and build a combined CEEMDAN . . .
  

   

  

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