DDFM: A Novel Perspective on Urban Travel Demand Forecasting Based on the Ensemble Empirical Mode Decomposition and Deep Learning.
Rongkun YeZhihao XuJunjie PangPublished in: ICBDT (2022)
Keyphrases
- deep learning
- empirical mode decomposition
- demand forecasting
- non stationary
- historical data
- unsupervised learning
- supply chain
- machine learning
- multi band
- mental models
- weakly supervised
- intrinsic mode functions
- learning algorithm
- feature selection
- forecasting accuracy
- random forests
- eeg signals
- neural network
- training set
- wavelet decomposition
- hybrid model
- multiresolution
- expert systems
- data mining