Long-term Spatio-Temporal Forecasting via Dynamic Multiple-Graph Attention
Authors: Wei Shao, Zhiling Jin, Shuo Wang, Yufan Kang, Xiao Xiao, Hamid Menouar, Zhaofeng Zhang, Junshan Zhang, Flora Salim
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two large-scale datasets demonstrate that our proposed approaches significantly improve the performance of existing graph neural network models in LSTF prediction tasks. We conduct extensive experiments on two large-scale public real-world spatio-temporal datasets. We validate the effectiveness of the proposed new graph models and fusion approaches using ablation studies. |
| Researcher Affiliation | Academia | 1School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, United States 2School of Telecommunications Engineering, Xidian University, Xi an, China 3D-ITET, ETH Zurich, Zurich, Switzerland 4School of Computing Technologies, RMIT University, Melbourne, Australia 5Qatar Mobility Innovations Center, Qatar University, Doha, Qatar 6ECE Department at University of California Davis, Davis, United States 7School of Computer Science and Engineering, University of New South Wales, Sydney, Australia |
| Pseudocode | Yes | Algorithm 1 Dynamic Multi-graph Fusion Input: Weight matrices: W D, W N, W F , W H, W T Parameter: Number of batches: Bt Output: Fused weight matrix W 1: Stack weight matrices to tensor T(0) R|G| N N. 2: Train T(0) while training ST-GNN models. 3: for i [0, Bt 1] do 4: T(i+1) T(i) 5: i = i + 1 6: W jk = P|G| i=1 T(i)(i, j, k), where W jk is the element of the weight matrix of the fused graph. 7: end for 8: return W |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code for its methodology or a link to a code repository. |
| Open Datasets | Yes | Parking: The Melbourne parking dataset, collected by the Melbourne City Council in 2019, contains 42, 672, 743 parking events recorded by the in-ground sensors every five minutes located in the Melbourne Central Business District (CBD) [Shao et al., 2017]. All sensors have been classified into 40 areas. https://data.melbourne.vic.gov.au/ Air Quality: The Ministry of Ecology and Environment of China (MEE) published a large-scale air quality dataset [Wang et al., 2020b], comprising 92 air quality monitoring stations, to assess the hourly PM2.5 concentration in Jiangsu province in 2020. https://english.mee.gov.cn/ |
| Dataset Splits | No | The paper mentions using a "24-time step historical time window" and "prediction horizons" but does not specify the train/validation/test dataset splits (e.g., percentages or absolute counts for each split). |
| Hardware Specification | Yes | Platform. All experiments were trained and tested on a Linux system (CPU: Intel(R) Xeon(R) Gold 6240 CPU @2.60GHz, GPU: NVIDIA Ge Force RTX 2080 Ti). |
| Software Dependencies | No | The paper mentions the use of the "Adam optimizer" and "L1 loss function" but does not provide specific version numbers for any software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages (e.g., Python 3.x). |
| Experiment Setup | Yes | Hyper-parameters. All the tests used a 24-time step historical time window, and the prediction horizons ranged from three to 24 steps. The proposed methods were optimized with the Adam optimizer. The learning rate was set to 1e 4. The L1 loss function was adopted to measure the performance of the proposed model. The batch size was 32, and the global seed was set to 0 for the experiment repeat. All the tests were trained for 40 epoches. The number of attention heads M and the dimension d of each attention head were set to 8 and 8 in the Parking dataset and set to 24 and 6 in the Air Quality dataset. |