Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks
Authors: Yijing Liu, Qinxian Liu, Jian-Wei Zhang, Haozhe Feng, Zhongwei Wang, Zihan Zhou, Wei Chen
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on two traffic datasets with prior structure and four benchmark datasets. The results indicate that TPGNN achieves the state-of-the-art on both short-term and long-term MTS forecastings. |
| Researcher Affiliation | Academia | 1 State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China {3150105531,22021050,zjw.cs,fenghz,wzw09,12121109,chenvis}@zju.edu.cn |
| Pseudocode | No | The paper describes its framework and inference pipeline but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Code is available at https://github.com/zyplanet/TPGNN. |
| Open Datasets | Yes | For the Traffic dataset, the sensor ID s coordinates are provided. We use these coordinates to construct the physical graph... PEMS-D7, PEMS-Bay are publicly available from PEMS [3]... The Traffic, Solar-Energy, Electricity, and Exchange-Rate are publicly available from [2, 19]... |
| Dataset Splits | Yes | We divide the dataset into three parts for training, validation, and testing with a ratio of 7:1:2. |
| Hardware Specification | Yes | All experiments are conducted on an NVIDIA GeForce RTX 3090 GPU. |
| Software Dependencies | Yes | We implement our models with PyTorch 1.10 and CUDA 11.3. |
| Experiment Setup | Yes | We tune the hyperparameters on the validation dataset, the results are presented in the Appendix A.7. The learning rate is set to 0.001, and the batch size is set to 32... |