H$$^2$$-Nets: Hyper-hodge Convolutional Neural Networks for Time-Series Forecasting

Chen, Yuzhou; Jiang, Tian; Gel, Yulia R.

doi:10.1007/978-3-031-43424-2_17

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…(10) Spatio-Temporal Graph ODE Networks (STGODE) [32]: STGODE for Traffic Flow Forecasting utilizes a continuous graph neural network for multivariate time series traffic forecasting. ( 11) Time Zigzags at Graph Convolutional Networks (Z-GCNETs) [45]: Z-GCNETs introduces the concept of zigzag persistence to time-aware graph convolutional networks for time series forecasting.…”

Section: Baseline Methodsmentioning

confidence: 99%

Adaptive Graph Convolutional Recurrent Network with Transformer and Whale Optimization Algorithm for Traffic Flow Prediction

Zhang,

Wu,

Shen

et al. 2024

Mathematics

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Accurate traffic flow prediction plays a crucial role in the development of intelligent traffic management. Despite numerous investigations into spatio-temporal methods, achieving high accuracy in traffic flow prediction remains challenging. This challenge arises from the complex dynamic spatio-temporal correlations within the traffic road network and the limitations imposed by the selection of hyperparameters based on experiments and manual experience, which can affect the performance of the network architecture. This paper introduces a novel transformer-based adaptive graph convolutional recurrent network. The proposed network automatically infers the interdependencies among different traffic sequences and incorporates the capability to capture global spatio-temporal correlations. This enables the dynamic capture of long-range temporal correlations. Furthermore, the whale optimization algorithm is employed to efficiently design an optimal network structure that aligns with the requirements of the traffic domain and maximizes the utilization of limited computational resources. This design approach significantly enhances the model’s performance and improves the accuracy of traffic flow prediction. The experimental results on four real datasets demonstrate the efficacy of our approach. In PEMS03, it improves MAE by 2.6% and RMSE by 1.4%. In PEMS04, improvements are 1.6% in MAE and 1.4% in RMSE, with a similar MAPE score to the best baseline. For PEMS07, our approach shows a 4.1% improvement in MAE and 2.2% in RMSE. On PEMS08, it surpasses the current best baseline with a 3.4% improvement in MAE and 1.6% in RMSE. These results confirm the good performance of our model in traffic flow prediction across multiple datasets.

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Section: Baseline Methodsmentioning

confidence: 99%

Adaptive Graph Convolutional Recurrent Network with Transformer and Whale Optimization Algorithm for Traffic Flow Prediction

Zhang,

Wu,

Shen

et al. 2024

Mathematics

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“…In recent years, with the development of deep neural network models, some works [4,[23][24][25] have proposed the use of Recurrent Neural Networks (RNNs) and their variants, such as Long Short-Term Memory (LSTM) [26] and Gated Recurrent Units (GRUs) [20,27], to capture the dynamic spatiotemporal dependencies in the traffic data, or combined the attention mechanisms [28,29] with RNN models to improve the fusion efficiency of dynamic spatiotemporal dependencies in the data. Additionally, some works [2,[30][31][32] have introduced Convolutional Neural Networks (CNNs) [33] to model the spatial correlations in the traffic data.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Spatial–Temporal Self-Attention Network for Traffic Flow Prediction

Wang,

Yang,

Zhou

2024

Future Internet

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Traffic flow prediction is considered to be one of the fundamental technologies in intelligent transportation systems (ITSs) with a tremendous application prospect. Unlike traditional time series analysis tasks, the key challenge in traffic flow prediction lies in effectively modelling the highly complex and dynamic spatiotemporal dependencies within the traffic data. In recent years, researchers have proposed various methods to enhance the accuracy of traffic flow prediction, but certain issues still persist. For instance, some methods rely on specific static assumptions, failing to adequately simulate the dynamic changes in the data, thus limiting their modelling capacity. On the other hand, some approaches inadequately capture the spatiotemporal dependencies, resulting in the omission of crucial information and leading to unsatisfactory prediction outcomes. To address these challenges, this paper proposes a model called the Dynamic Spatial–Temporal Self-Attention Network (DSTSAN). Firstly, this research enhances the interaction between different dimension features in the traffic data through a feature augmentation module, thereby improving the model’s representational capacity. Subsequently, the current investigation introduces two masking matrices: one captures local spatial dependencies and the other captures global spatial dependencies, based on the spatial self-attention module. Finally, the methodology employs a temporal self-attention module to capture and integrate the dynamic temporal dependencies of traffic data. We designed experiments using historical data from the previous hour to predict traffic flow conditions in the hour ahead, and the experiments were extensively compared to the DSTSAN model, with 11 baseline methods using four real-world datasets. The results demonstrate the effectiveness and superiority of the proposed approach.

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