Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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 EMAIL |
| 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... |