Training LSTMS with circular-shift epochs for accurate event forecasting in imbalanced time series

Chen, Xiaoqian; Gupta, Lalit

doi:10.1016/j.eswa.2023.121701

Cited by 1 publication

(1 citation statement)

References 42 publications

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“…RNNs are prone to gradient disappearance and explosion, while LSTM can effectively solve the problems of RNNs and can capture the features of long-time sequences in a more comprehensive way (Ameri, et al, 2023;Chen & Gupta, 2024). Zhao et al (2021) (Zhang, S., et al, 2022) can consider the spatio-temporal dependence of traffic networks, especially the topological structure information among stations, roads, and regions, and non-Euclidean traffic data's structural information can be fully utilized, and GCN also has faster training efficiency and fewer hyperparameters compared with RNN and CNN.…”

Section: Related Workmentioning

confidence: 99%

A Passenger Flow Prediction Method Using SAE-GCN-BiLSTM for Urban Rail Transit

Liu

2023

International Journal of Swarm Intelligence Research

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To address the problems of existing passenger flow prediction methods such as low accuracy, inadequate learning of spatial features of station topology, and inability to apply to large networks, a SAE-GCN-BiLSTM-based passenger flow forecasting method for urban rail transit is proposed. First, the external features are extracted layer by layer using stacked autoencoder (SAE). Then, graph convolutional network (GCN) is used to capture the spatial features of station topology, and bi-directional long and short-term memory network (BiLSTM) is used to extract the bi-directional temporal features, realizing the extraction of the spatio-temporal features. Finally, external features and spatio-temporal features are fused for accurate prediction of urban rail transit passenger flow. The experimental results show that the proposed method is higher than several other advanced models in the evaluation indexes under different granularities, indicating that the model effectively develops the accuracy and robustness of urban rail transit passenger flow prediction.

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Section: Related Workmentioning

confidence: 99%

A Passenger Flow Prediction Method Using SAE-GCN-BiLSTM for Urban Rail Transit

Liu

2023

International Journal of Swarm Intelligence Research

View full text Add to dashboard Cite

show abstract

Training LSTMS with circular-shift epochs for accurate event forecasting in imbalanced time series

Cited by 1 publication

References 42 publications

A Passenger Flow Prediction Method Using SAE-GCN-BiLSTM for Urban Rail Transit

A Passenger Flow Prediction Method Using SAE-GCN-BiLSTM for Urban Rail Transit

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