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.
SaSDim:Self-Adaptive Noise Scaling Diffusion Model for Spatial Time Series Imputation
Authors: Shunyang Zhang, Senzhang Wang, Xianzhen Tan, Renzhi Wang, Ruochen Liu, Jian Zhang, Jianxin Wang
IJCAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 17,471 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,167 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 conducted on three real-world spatial time series datasets verify the effectiveness of Sa SDim. |
| Researcher Affiliation | Academia | Central South University EMAIL, |
| Pseudocode | Yes | Algorithm 1 Training of Sa SDi M and Algorithm 2 Imputation (Sampling) with Sa SDi M |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating the public release of source code for the described methodology. |
| Open Datasets | Yes | We evaluate the performance of our model on three spatial time series datasets, METR-LA, AQI-36, and PEMS-BAY. AQI-36 is collected from 36 AQI sensors distributed across the city of Beijing. This dataset serves as a widely recognized benchmark for imputation techniques and includes a mask used for evaluation that simulates the distribution of actual missing data [Yi et al., 2016]. |
| Dataset Splits | Yes | For the two datasets METR-LA and PEMS-BAY, we partition the entire data into training, validation, and testing sets by a ratio of 8 : 1: 1. |
| Hardware Specification | No | This work was carried out in part using computing resources at the High-Performance Computing Center of Central South University. |
| Software Dependencies | No | The model is implemented using Pytorch and trained in an end-to-end manner using Adam with a learning rate of 0.001. |
| Experiment Setup | Yes | Table 2: The settings of Sa SDi M for the three datasets. Description AQI-36 METR-LA PEMS-BAY Batch size 16 16 16 Time length L 24 24 24 Epochs 200 200 200 Learning rate 0.001 0.001 0.001 Channel size d 64 64 64 Minimum noise level β1 0.0001 0.0001 0.0001 Maximum noise level βT 0.5 0.2 0.2 Diffusion steps T 50 50 50 |