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 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 {224712166, szwang, 224712155, rzwang, 234712122, jianzhang, jxwang}@csu.edu.cn, |
| 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 |