Multi-stage deep residual collaboration learning framework for complex spatial–temporal traffic data imputation

Li, Jinlong; Li, Ruonan; Xu, Lunhui

doi:10.1016/j.asoc.2023.110814

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…The GCN-LSTM can effectively learn and integrate both the structural and temporal characteristics of dynamic networks. This, in turn, enables it to make predictions regarding the future addition and removal of network links [28]. Hence, the GCN-LSTM network is proficient at leveraging high-dimensional, time-dependent, and sparsely structured sequential data as its input.…”

Section: Lstm Network and Their Hybrid Modelsmentioning

confidence: 99%

Optimization Hybrid of Multiple-Lag LSTM Networks for Meteorological Prediction

Zhu,

Zhang,

Crabbe

et al. 2023

Mathematics

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Residences in poor regions always depend on rain-fed agriculture, so they urgently need suitable tools to make accurate meteorological predictions. Unfortunately, meteorological observations in these regions are usually sparse and irregularly distributed. Conventional LSTM networks only handle temporal sequences and cannot utilize the links of meteorological variables among stations. GCN-LSTM networks only capture local spatial structures through the simple structures of fixed adjacency matrices, and the CNN-LSTM can only mine gridded meteorological observations for further predictions. In this study, we propose an optimization hybrid of multiple-lag LSTM networks for meteorological predictions. Our model can make full use of observed data at partner stations under different time-lag windows and strong links among the local observations of meteorological variables to produce future predictions. Numerical experiments on the meteorological predictions of Bangladesh demonstrate that our networks are superior to the classic LSTM and its variants GCN-LSTM and CNN-LSTM, as well as the SVM and DT.

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Section: Lstm Network and Their Hybrid Modelsmentioning

confidence: 99%