Open AccessRailway freight volume forecasting based on combined DWT-Bi-LSTM model
Author(s) -
Yue-Ying Qiu,
Ming Lei
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1601/3/032039
Subject(s) - computer science , volume (thermodynamics) , wavelet transform , discrete wavelet transform , wavelet , artificial intelligence , data mining , pattern recognition (psychology) , machine learning , physics , quantum mechanics
Railway freight volume forecasting can provide information for National Railway Department to establish effective operation lines, and its research plays an important role in increasing the effective proportion of railway transport. In order to improve the prediction accuracy, this paper proposes a combined model based on wavelet transform (WT) and bidirectional long short-term memory (Bi-LSTM) for high-precision railway freight volume forecasting. In this method, experimental data are first denoised by wavelet transform to extract important signal features that can accurately express the data information, and then Bi-LSTM is utilized to learn from the historical denoised data and iteratively improve upon predictions. To verify the improved prediction performance of DWT-Bi-LSTM, results are compared with those of the single LSTM, GRU, Bi-LSTM model along with the LSTM, GRU model combined with wavelet transform. The experimental results show that the combined DWT-Bi-LSTM model proposed in this paper has higher accuracy in the prediction of railway freight volume than the other forecasting methods.