Exploring explicit and implicit graph learning for multivariate time series imputation
Engineering Applications of Artificial Intelligence, ISSN: 0952-1976, Vol: 127, Page: 107217
2024
- 3Citations
- 9Captures
- 1Mentions
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Most Recent News
New Findings from University of Technology Sydney in Technology Provides New Insights (Exploring Explicit and Implicit Graph Learning for Multivariate Time Series Imputation)
2024 JAN 02 (NewsRx) -- By a News Reporter-Staff News Editor at Tech Daily News -- Investigators discuss new findings in Technology. According to news
Article Description
Multivariate time series inherently contain missing values due to various issues, including incorrect data entry, broken equipment, and package loss during data transferring. The successful completion of time series data analysis tasks heavily relies on the essential task of imputing missing values. Inter-variable relationships in time series are typically overlooked by missing value imputation techniques. Although some graph-based algorithms can capture these relationships, the design of graph structures is commonly handcrafted and dataset-centric. We introduce a novel E xplicit and I mplicit G raph R ecurrent N etwork (EIGRN) for multivariate time series imputation that integrates graph and recurrent neural networks to capture variable and time dependencies together. This proficiency is achieved by effectively integrating external data sources such as domain knowledge and the implicit relationships among nodes. In order to make our approach more applicable to datasets with larger numbers of missing values, we additionally discuss the model’s performance for various missing value ratios. Our comprehensive experiments on real-world datasets show that our model outperforms state-of-the-art baselines in different industrial fields.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S095219762301401X; http://dx.doi.org/10.1016/j.engappai.2023.107217; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85173286796&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S095219762301401X; https://dx.doi.org/10.1016/j.engappai.2023.107217
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know