ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data
Authors: Xiuwen Yi, Yu Zheng, Junbo Zhang, Tianrui Li
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method based on Beijing air quality and meteorological data, finding advantages to our model compared with ten baseline approaches. |
| Researcher Affiliation | Collaboration | 1School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China 2Microsoft Research, Beijing, China 3Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1 ST-MVL Input: Original Data Matrix 𝑀, 𝜔, 𝛼, 𝛽; Output: Final Data Matrix; |
| Open Source Code | Yes | The code and datasets have been released at: http://research.microsoft.com/apps/pubs/?id=264768. |
| Open Datasets | Yes | We evaluate our model based on two real datasets: air quality and meteorological data in Beijing from 2014/05/01 to 2015/04/30 [Zheng et al., 2015] |
| Dataset Splits | No | We partition the 1-year data into two parts, using the 3rd, 6th, 9th and 12th months as a test set and the rest for a training set. The paper does not mention a distinct validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Parameter Settings: We test different 𝛼 for IDW, 𝛽 for SES, and 𝜔 for UCF & ICF, finding a best setting for them, e.g. when 𝛼=4, 𝛽=0.85, and 𝜔=7 achieve the best performance in PM2.5 property. |