DSANet

Huang, Siteng; Wang, Donglin; Wu, Xuehan; Tang, Ao

doi:10.1145/3357384.3358132

Cited by 172 publications

(23 citation statements)

References 5 publications

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“…DSANet [38]: A multivariate time series prediction model that uses CNN to capture the temporal correlations of a time series and captures the spatial correlations using a self-attention mechanism.…”

Section: Benchmarking Methodsmentioning

confidence: 99%

Spatio‐temporal adaptive graph convolutional networks for traffic flow forecasting

Wei

Gao

et al. 2022

IET Intelligent Trans Sys

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Accurate forecasting of traffic flow is crucial for intelligent traffic control and guidance. It is very challenging to forecast the traffic flow due to the high non‐linearity, complexity and dynamicity of the data. Most existing forecasting methods focus on designing complicated graph neural network architectures to capture the spatio‐temporal features of traffic data with the help of predefined graphs. However, traffic data exhibit a strong spatial dependency, which means that there are often complex correlations between nodes in a road network topology graph. Moreover, the spatial correlations of the road network change over time. To capture the properties of the road network topology graph, a novel spatio‐temporal adaptive graph convolutional networks model (STAGCN) based on deep learning is proposed. Utilize adaptive graph generation block to capture the static and dynamic structures of the traffic road network respectively, and they are integrated to construct an adaptive road network topology graph. Then the spatio‐temporal features of the traffic data are captured using spatio‐temporal convolution blocks. Experiments were conducted on publicly available traffic datasets for freeway traffic in California, USA, and the results showed that the prediction accuracy of the STAGCN model outperformed multiple baseline methods.

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Section: Benchmarking Methodsmentioning

confidence: 99%

Spatio‐temporal adaptive graph convolutional networks for traffic flow forecasting

Wei

Gao

et al. 2022

IET Intelligent Trans Sys

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“…For TPA-LSTM and DSANet, we followed the parameter settings as reported in the TPA paper 29 and the DSANet paper. 30 For the training of the LGEANet, we first generate a pre-trained network optimized using tumor trajectories of different patients. And for each trace in the evaluation dataset, the first 50 min were used as the training period for transfer learning to build the patient-specific prediction model.…”

Section: Experimental Settings and Implementation Detailsmentioning

confidence: 99%

“…DSANet 30 utilizes the strong feature-extraction capability of self -attentional networks to learn its relationship with other learned representations including itself for each learned representation of a univariate series. A dual-stage attention-based Conv-LSTM network 31 was proposed to learn the spatio-temporal correlation of multivariate time series.…”

Section: Introductionmentioning

confidence: 99%

LGEANet: LSTM‐global temporal convolution‐external attention network for respiratory motion prediction

et al. 2023

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To develop a deep learning network that treats the three-dimensional respiratory motion signals as a whole and considers the inter-dimensional correlation between signals of different directions for accurate respiratory tumor motion prediction. Methods: We propose a deep learning framework, named as LSTM-Global Temporal Convolution-External Attention Network (LGEANet). In LGEANet, we first feed each of the univariate time series into the Long Short-Term Memory (LSTM) module respectively and utilize the strength of the global temporal convolutional layer to discover the temporal pattern of the univariate signals from hidden states of the LSTM. Then, External attention is adopted to capture the dynamic dependence of the multiple time series. Also, a traditional autoregressive linear model in parallel to the non-linear neural network part was integrated to mitigate the scale insensitivity of the networks. A total of 304 motion traces for 31 patients are acquired from a public dataset in the experiments and four representative cases were selected for model evaluation. The respiratory signals were sampled at intervals of about 37.5 ms (26 frames per second) for an average duration of 71 min. Results: The proposed LGEANet achieved better performance with higher empirical correlation coefficient value (CORRs) and lower mean absolute error value (MAEs) and relative squared error value (RSEs) than other investigated models. For the four representative datasets, when the response time is less than 231 ms, the model can achieve CORRs more than 0.96. And the averaged position error reduction by using the proposed model was about 67% in the superior-inferior (SI) direction, 41% in the anterior-posterior (AP) direction and 38% in the right-left (RL) direction compared to that without prediction. The proposed network achieved the greatest error reduction in the SI direction, which is the main direction of tumor motion. Conclusions:The LGEANet achieves promising performance in minimizing the prediction error due to system latencies during real-time tumor motion tracking.

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“…Autoregressive model ensembling.Due to the nonlinearity of convolutional, multi-head self-attention, and GRU modules, the output is not sensitive to the original input [23]. To address this drawback, we apply a first-order autoregressive (AR) model to the preprocessed data to obtain the second predicted value.…”

Section: Modules Of Cgnn-mhsa-armentioning

confidence: 99%

Identifying performance anomalies in fluctuating cloud environments: A robust correlative-GNN-based explainable approach

Song

Xin

Chen

et al. 2023

Future Generation Computer Systems

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DSANet

Cited by 172 publications

References 5 publications

Spatio‐temporal adaptive graph convolutional networks for traffic flow forecasting

Spatio‐temporal adaptive graph convolutional networks for traffic flow forecasting

LGEANet: LSTM‐global temporal convolution‐external attention network for respiratory motion prediction

Identifying performance anomalies in fluctuating cloud environments: A robust correlative-GNN-based explainable approach

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