Information Bottleneck-guided MLPs for Robust Spatial-temporal Forecasting
Authors: Min Chen, Guansong Pang, Wenjun Wang, Cheng Yan
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experimental results show that an excellent trade-off between the robustness and the efficiency can be achieved by RSTIBMLP compared to state-of-the-art STGNNs and MLP models. Comprehensive experiments on STF benchmark datasets from various domains under both noisy and clean evaluations demonstrate that 1) RSTIB-MLP achieves better, or comparably good, robustness, compared with state-of-the-art (SOTA) STGNNs, while being substantially more computationally efficient, and 2) RSTIB-MLP is much more robust than SOTA MLP-based models while being comparably efficient. |
| Researcher Affiliation | Academia | Min Chen * 1 Guansong Pang * 2 Wenjun Wang 1 Cheng Yan 1 ... 1Tianjin University 2Singapore Management University. Correspondence to: Cheng Yan <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 RSTIB-MLP for Spatial-Temporal Forecasting |
| Open Source Code | Yes | Our code is publicly available at https://github.com/mchen644/RSTIB. |
| Open Datasets | Yes | Datasets. For demonstrating universality, we consider six datasets from different domains, including PEMS04, PEMS07, PEMS08 (Fang et al., 2021; Guo et al., 2019; Song et al., 2020; Yu et al., 2018), Large ST(SD) (Liu et al., 2024a), Weather2K-R (Zhu et al., 2023b), Electricity (Deng et al., 2021). ... Public accessed data can be found in (Guo et al., 2021b): https://github.com/guoshnBJTU/ASTGNN/tree/main/data ... Large ST (Liu et al., 2024a): It is publicly available at https://github.com/liuxu77/Large ST. Weather2K-R (Zhu et al., 2023b): It is publicly available at https://github.com/bycnfz/weather2k. Electricity (Deng et al., 2021): It is publicly available at https://github.com/JLDeng/ST-Norm. |
| Dataset Splits | Yes | For Electricity dataset, we adopt the same training, validation, and testing split ratio as in (Deng et al., 2021), and for other datasets, we adopt 6:2:2 for all datasets to ensure consistency. |
| Hardware Specification | Yes | All evaluations are conducted on an NVIDIA RTX 3090Ti GPU. ... The models are trained on NVIDIA Ge Force RTX 3090Ti GPUs |
| Software Dependencies | Yes | We adopt Py Torch 1.13.1 on NVIDIA RTX 3090Ti GPUs, utilizing the Py Torch framework (Paszke et al., 2019). |
| Experiment Setup | Yes | Implementation Details. For the basic settings, we employ a hidden dimension d = 64 and utilize an MLP architecture with L=3 layers. For PEMS and Large TS(SD) benchmark datasets, we use historical traffic flow data with window length P = 12 to forecast future traffic flow data with window length F = 12, while for the Electricity dataset, we follow the default settings in (Deng et al., 2021), i.e., we set P=16 and F=3, and calculate the average predictive accuracy by averaging over 1, 2, 3 hours. ... The learning rate is initialized as η = 0.002 with a decay factor r = 0.5. ... A summary of the default hyperparameter settings is in Table 9. |