Motif-aware Graph Neural Networks for Networked Time Series Imputation
Authors: Nourhan Ahmed, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that when compared to state-of-the-art models for time-series imputation tasks, our proposed model can reduce the error by around 19%. Experiment. Empirical results show that Motif-GNN outperforms baseline models by approximately 19% for time series imputation across various real-world datasets. Evaluation is conducted using Mean Absolute Error (MAE) and Mean Relative Error (MRE) as metrics. |
| Researcher Affiliation | Collaboration | 1 Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Germany 2 VWFS Data Analytics Research Center EMAIL |
| Pseudocode | Yes | Algorithm 1: The Framework of Motif-GNN. |
| Open Source Code | No | No information about open-source code is provided in the paper. There are no links to repositories or explicit statements about code release. |
| Open Datasets | Yes | The air quality dataset comprises observations from 36 monitoring sites (Yi et al. 2016), collected between May 1, 2014, and April 30, 2015, totaling 8,759 timestamps (Yi et al. 2016; Zheng et al. 2015). Traffic Datasets. The traffic datasets used in this paper are Pe MS-LA, Pe MS-BA, and Pe MS-SD, collected from January 1 to March 30, 2022, from highways in Los Angeles County, the Bay Area, and San Diego, respectively (Chen et al. 2001). |
| Dataset Splits | Yes | Under the MCAR scenario, datasets are split into 70% for training, 10% for validation, and 20% for testing. In the block-missing scenario, we simulate overlapping time- and space-structured (spatio-temporal) missingness by randomly dropping 5% of the data per sensor and simulating failures with a 0.15% probability, varying the duration from 1 to 4 hours for traffic data and 2 to 6 days for air quality data, following the evaluation protocol in (Cini, Marisca, and Alippi 2022). Additionally, to ensure consistency with baseline models, we consider time-structured missingness in the air quality dataset by partitioning the 1-year data into two parts, using the months of March, June, September, and December as the test set and the rest for training (Yi et al. 2016; Cini, Marisca, and Alippi 2022; Wang et al. 2023a). For traffic datasets, we used the first half of February and the last half of March for testing and the rest for training. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers, such as programming language versions or library versions (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings in the main text. |