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..
Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
Authors: Haotian Gao, Renhe Jiang, Zheng Dong, Jinliang Deng, Yuxin Ma, Xuan Song
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A series of quantitative and qualitative evaluations on four widely used benchmarks (PEMS03, PEMS04, PEMS07, and PEMS08) are conducted to validate the state-of-the-art performance of STD-MAE. |
| Researcher Affiliation | Academia | Haotian Gao1,2 , Renhe Jiang1 , Zheng Dong2 , Jinliang Deng3 , Yuxin Ma2 , Xuan Song2 1The University of Tokyo 2Southern University of Science and Technology 3University of Technology Sydney |
| Pseudocode | No | The paper does not include a dedicated section or figure explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Codes are available at https://github.com/ Jimmy-7664/STD-MAE. |
| Open Datasets | Yes | To thoroughly evaluate the proposed STD-MAE model, we conduct extensive experiments on four real-world spatiotemporal benchmark datasets as listed in Table 1: PEMS03, PEMS04, PEMS07, and PEMS08 [Song et al., 2020]. |
| Dataset Splits | Yes | Following previous work [Song et al., 2020; Li and Zhu, 2021; Fang et al., 2021; Jiang et al., 2023a; Guo et al., 2021b], we divide the four datasets into training, validation, and test sets according to a 6:2:2 ratio. |
| Hardware Specification | Yes | Experiments are mainly conducted on a Linux server with four NVIDIA Ge Force RTX 3090 GPUs. |
| Software Dependencies | No | The paper mentions that experiments are performed on the 'Basic TS [Shao et al., 2023] platform' but does not specify version numbers for other general software dependencies like Python, PyTorch/TensorFlow, or CUDA. |
| Experiment Setup | Yes | The embedding dimension D is 96. The encoder has 4 transformer layers while the decoder has 1 transformer layer. The number of multi-attention heads in transformer layer is set to 4. We use a patch size L of 12 to align with the forecasting input. T is equal to 1, which means we truncate and keep the last one patch of H(S) and H(T ). The masking ratio r is set to 0.25. Optimization is performed with Adam optimizer using an initial learning rate of 0.001 and mean absolute error (MAE) loss. |