Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data
Authors: Yucheng Wang, Yuecong Xu, Jianfei Yang, Min Wu, Xiaoli Li, Lihua Xie, Zhenghua Chen
AAAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 19,607 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,141 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show the effectiveness of FC-STGNN on multiple MTS datasets compared to SOTA methods. We examine our method on three different downstream tasks: Remaining Useful Life (RUL) prediction, Human Activity Recognition (HAR), and Sleep Stage Classification (SSC). Specifically, we utilize C-MAPSS (Saxena et al. 2008) for RUL prediction, UCI-HAR (Anguita et al. 2012) for HAR, and ISRUC-S3 (Khalighi et al. 2016) for SSC, following the previous work (Wang et al. 2023a). |
| Researcher Affiliation | Collaboration | 1Institute for Infocomm Research, A*STAR, Singapore 2Centre for Frontier AI Research, A*STAR, Singapore 3Nanyang Technological University, Singapore |
| Pseudocode | No | The paper does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | The code is available at https://github.com/Frank-Wang-oss/FCSTGNN. |
| Open Datasets | Yes | Specifically, we utilize C-MAPSS (Saxena et al. 2008) for RUL prediction, UCI-HAR (Anguita et al. 2012) for HAR, and ISRUC-S3 (Khalighi et al. 2016) for SSC, following the previous work (Wang et al. 2023a). |
| Dataset Splits | Yes | For C-MAPSS which includes four sub-datasets, we adopt the pre-defined train-test splits. The training dataset is further divided into 80% and 20% for training and validation. For HAR and ISRUC, we randomly split them into 60%, 20%, and 20% for training, validating, and testing. |
| Hardware Specification | Yes | All methods are conducted with NVIDIA Ge Force RTX 3080Ti and implemented by Py Torch 1.9. |
| Software Dependencies | Yes | All methods are conducted with NVIDIA Ge Force RTX 3080Ti and implemented by Py Torch 1.9. |
| Experiment Setup | Yes | We set the batch size as 100, choose ADAM as the optimizer with a learning rate of 1e-3, and train the model 40 epochs. |