SAITS: Self-Attention-based Imputation for Time Series [PDF]
Expert systems with applications, 2022 Missing data in time series is a pervasive problem that puts obstacles in the way of advanced analysis. A popular solution is imputation, where the fundamental challenge is to determine what values should be filled in.
Wenjie Du, David Cote, Y. Liu
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Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models [PDF]
Trans. Mach. Learn. Res., 2022 The imputation of missing values represents a significant obstacle for many real-world data analysis pipelines. Here, we focus on time series data and put forward SSSD, an imputation model that relies on two emerging technologies, (conditional) diffusion
Juan Miguel Lopez Alcaraz, N. Strodthoff
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PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation [PDF]
IEEE International Conference on Data Engineering, 2023 Spatiotemporal data mining plays an important role in air quality monitoring, crowd flow modeling, and climate forecasting. However, the originally collected spatiotemporal data in real-world scenarios is usually incomplete due to sensor failures or ...
Mingzhe Liu +5 more
semanticscholar +1 more source
MissForest - non-parametric missing value imputation for mixed-type data [PDF]
Bioinform., 2011 MOTIVATION Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set.
D. Stekhoven, Peter Bühlmann
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ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation [PDF]
Knowledge Discovery and Data Mining, 2023 Missing data is a pervasive issue in both scientific and engineering tasks, especially for the modeling of spatiotemporal data. Existing imputation solutions mainly include low-rank models and deep learning models.
Tong Nie +4 more
semanticscholar +1 more source
Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking [PDF]
Journal of Biomedical Informatics, 2023 The imputation of missing values in multivariate time series (MTS) data is a critical step in ensuring data quality and producing reliable data-driven predictive models.
Maksims Kazijevs, Manar D. Samad
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Applied Sciences, 2022 The aim of this study is to ascertain the most suitable model for predicting complex odors using odor substance data that has a small number of data and a large number of missing data.
Do-Hyun Lee +3 more
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Bayesian Estimation of Disclosure Risks for Multiply Imputed, Synthetic Data
The Journal of Privacy and Confidentiality, 2014 Agencies seeking to disseminate public use microdata, i.e., data on individual records, can replace confidential values with multiple draws from statistical models estimated with the collected data.
Jerome P. Reiter +2 more
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Integration of Multimodal Data from Disparate Sources for Identifying Disease Subtypes
Biology, 2022 Studies over the past decade have generated a wealth of molecular data that can be leveraged to better understand cancer risk, progression, and outcomes.
Kaiyue Zhou +5 more
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Evaluating the Accuracy of Imputation Methods in a Five-Way Admixed Population
Frontiers in Genetics, 2019 Genotype imputation is a powerful tool for increasing statistical power in an association analysis. Meta-analysis of multiple study datasets also requires a substantial overlap of SNPs for a successful association analysis, which can be achieved by ...
Haiko Schurz +9 more
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