ArticleEntropy (Basel, Switzerland)2023
ImputeGAN: Generative Adversarial Network for Multivariate Time Series Imputation.
Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Blue Noise-based Generative Models for the Imputation of Time-series Data.International Conference on Information Science and Technology. International Conference on Information Science and Technology · 2025Article
- Self-Attention-Based Deep Learning for Missing Sensor Data Imputation in Real-Time Probe Card Monitoring.Sensors (Basel, Switzerland) · 2025Article
- Reconstructing Dynamic Gene Regulatory Networks Using f-Divergence from Time-Series scRNA-Seq Data.Current issues in molecular biology · 2025Article
- Bidirectional f-Divergence-Based Deep Generative Method for Imputing Missing Values in Time-Series Data.Stats · 2025Article
- tBN-CSDI: a time-varying blue noise-based diffusion model for time-series imputation.Bioinformatics advances · 2025Article
- Application of machine learning techniques for warfarin dosage prediction: a case study on the MIMIC-III dataset.PeerJ. Computer science · 2025Article
- The impact of missing data rates and imputation methods on the assumption of unidimensionality.PloS one · 2025Article
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Authors and funding
2 authors.
Funding
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Abstract
Since missing values in multivariate time series data are inevitable, many researchers have come up with methods to deal with the missing data. These include case deletion methods, statistics-based imputation methods, and machine learning-based imputation methods. However, these methods cannot handle temporal information, or the complementation results are unstable. We propose a model based on generative adversarial networks (GANs) and an iterative strategy based on the gradient of the complementary results to solve these problems. This ensures the generalizability of the model and the reasonableness of the complementation results. We conducted experiments on three large-scale datasets and compare them with traditional complementation methods. The experimental results show that imputeGAN outperforms traditional complementation methods in terms of accuracy of complementation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.