Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques
Objective The proper handling of missing values is critical to delivering reliable estimates
and decisions, especially in high-stakes fields such as clinical research. In response to the …
and decisions, especially in high-stakes fields such as clinical research. In response to the …
Tsi-bench: Benchmarking time series imputation
Effective imputation is a crucial preprocessing step for time series analysis. Despite the
development of numerous deep learning algorithms for time series imputation, the …
development of numerous deep learning algorithms for time series imputation, the …
Robust imputation method with context-aware voting ensemble model for management of water-quality data
Water-quality monitoring and management are crucial for ensuring the safety and
sustainability of water resources. However, missing data is a frequent problem in water …
sustainability of water resources. However, missing data is a frequent problem in water …
Application of a deep learning-based discrete weather data continuousization model in ship route optimization
Z Wu, S Wang, Q Yuan, N Lou, S Qiu, L Bo, X Chen - Ocean Engineering, 2023 - Elsevier
When ships sail across oceans, weather and sea conditions are the key factors affecting the
safety and economy of ship navigation. Thus, obtaining accurate weather forecast data is …
safety and economy of ship navigation. Thus, obtaining accurate weather forecast data is …
Causality-aware spatiotemporal graph neural networks for spatiotemporal time series imputation
Spatiotemporal time series are usually collected via monitoring sensors placed at different
locations, which usually contain missing values due to various failures, such as mechanical …
locations, which usually contain missing values due to various failures, such as mechanical …
Parallel Generative Adversarial Imputation Network for Multivariate Missing Time-Series Reconstruction and Its Application to Aero-Engines
S Ma, ZS Xu, T Sun - IEEE Transactions on Instrumentation and …, 2023 - ieeexplore.ieee.org
The reconstruction and imputation of missing values in multivariate time series (MTS) are
pressing issues in the field of industrial artificial intelligence. To address this problem, an …
pressing issues in the field of industrial artificial intelligence. To address this problem, an …
BERT (Bidirectional Encoder Representations from Transformers) for missing data imputation in solar irradiance time series
The availability of solar irradiance time series without missing data is an ideal scenario for
researchers in the field. However, it is not achievable for a variety of reasons, such as …
researchers in the field. However, it is not achievable for a variety of reasons, such as …
Data analysis and preprocessing techniques for air quality prediction: a survey
C Yu, J Tan, Y Cheng, X Mi - Stochastic Environmental Research and Risk …, 2024 - Springer
Air quality prediction technology can provide effective technical means for environmental
governance. In recent years, due to the strong nonlinearity of data, there has been extensive …
governance. In recent years, due to the strong nonlinearity of data, there has been extensive …
The impact of data imputation on air quality prediction problem
With rising environmental concerns, accurate air quality predictions have become
paramount as they help in planning preventive measures and policies for potential health …
paramount as they help in planning preventive measures and policies for potential health …
[HTML][HTML] A Comparative Study on Imputation Techniques: Introducing a Transformer Model for Robust and Efficient Handling of Missing EEG Amplitude Data
MA Khan - Bioengineering, 2024 - mdpi.com
In clinical datasets, missing data often occur due to various reasons including non-response,
data corruption, and errors in data collection or processing. Such missing values can lead to …
data corruption, and errors in data collection or processing. Such missing values can lead to …