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Time Series Data Imputation: A Survey on Deep Learning Approaches [article]

Chenguang Fang, Chen Wang
2020 arXiv   pre-print
However, unexpected accidents for example broken sensors or missing of the signals will cause missing values in time series, making the data hard to be utilized.  ...  Currently, time series data imputation is a well-studied problem with different categories of methods.  ...  The specific models may relate to the basic idea of the methods. • Auto-Encoder Enhanced : auto-encoder structure is an approach that can be applied in the imputation of the data.  ... 
arXiv:2011.11347v1 fatcat:a27t7fsu7bcbxkwjamcjpxhzea

Imaging Time-Series to Improve Classification and Imputation [article]

Zhiguang Wang, Tim Oates
2015 arXiv   pre-print
The imputation MSE on test data is reduced by 12.18%-48.02% when compared to using the raw data.  ...  Inspired by the bijection property of GASF on 0/1 rescaled data, we train Denoised Auto-encoders (DA) on the GASF images of four standard and one synthesized compound dataset.  ...  Table 2 : 2 MSE of imputation on time series using raw data and GASF images.  ... 
arXiv:1506.00327v1 fatcat:kluaxnd4wzhthlwprikgdljrse

Semi-supervised Learning with Missing Values Imputation [article]

Buliao Huang and Yunhui Zhu and Muhammad Usman and Huanhuan Chen
2021 arXiv   pre-print
Moreover, SSCFlow treats the initialized missing values as corrupted initial imputation and iteratively reconstructs their latent representations with an overcomplete denoising autoencoder to approximate  ...  Missing values imputation methods are often employed to replace the missing values with substitute values.  ...  BACKGROUND: NORMALIZING FLOW SSCFlow builds on normalizing flow [7] - [9] , a type of deep generative model as variational auto-encoder (VAE) [20] and generative adversarial nets (GAN) [21] .  ... 
arXiv:2106.01708v2 fatcat:4fr74ttuljgmxiexjrva2too7e

Neural ODEs for Informative Missingness in Multivariate Time Series [article]

Mansura Habiba, Barak A. Pearlmutter
2020 arXiv   pre-print
Such missing observations are one of the major limitations of time series processing using deep learning.  ...  Informative missingness is unavoidable in the digital processing of continuous time series, where the value for one or more observations at different time points are missing.  ...  . • Another practice is to apply data imputation multiple times iteratively with a target to reach an average value for the missing observations.  ... 
arXiv:2005.10693v1 fatcat:7yuqk23syjgplgp4txceufv7xm

Comparison of Data Imputation Techniques and their Impact [article]

Darren Blend, Tshilidzi Marwala
2008 arXiv   pre-print
Missing and incomplete information in surveys or databases can be imputed using different statistical and soft-computing techniques.  ...  The global impact assessment of the imputed data is performed by several statistical tests.  ...  The principal component information provides an indication to the global impact that imputation of data has not only to the missing data columns, but to the dynamics of the entire dataset. 5) Data Classification  ... 
arXiv:0812.1539v1 fatcat:kir4e5c4abdrvc6ibwq6cmpn6i

Proposition of a Theoretical Model for Missing Data Imputation using Deep Learning and Evolutionary Algorithms [article]

Collins Leke, Tshilidzi Marwala, Satyakama Paul
2015 arXiv   pre-print
In the last couple of decades, there has been major advancements in the domain of missing data imputation.  ...  In this article, considering arbitrary and monotone missing data patterns, we hypothesize that the use of deep neural networks built using autoencoders and denoising autoencoders in conjunction with genetic  ...  most 87.3% on the imputation of discrete values with varying ratios of missingness.  ... 
arXiv:1512.01362v1 fatcat:7acpzpf7gfcjxk2wa2bzbmndha

Reconstruction of time series with missing value using 2D representation-based denoising autoencoder

Tao Huamin, Deng Qiuqun, Xiao Shanzhu
2020 Journal of Systems Engineering and Electronics  
This work provides useful insights into the better reconstruction of missing values in time series analysis to considerably improve the reliability of timevarying system.  ...  In this study, we propose a novel time series data representation-based denoising autoencoder (DAE) for the reconstruction of missing values.  ...  Mean imputation replaces the missing values with the mean value of the adjacent observations. AR imputation uses the auto-regression model (p = 3) to fill in missing values.  ... 
doi:10.23919/jsee.2020.000081 fatcat:zn6gg5wponaafg6oeblb7c7llq

Identifying Sepsis Subphenotypes via Time-Aware Multi-Modal Auto-Encoder [article]

Changchang Yin, Ruoqi Liu, Dongdong Zhang, Ping Zhang
2020 medRxiv   pre-print
, medications, lab tests and vital signs) to impute missing values, a dynamic time wrapping (DTW) method to measure patients' temporal similarity based on the imputed EHR data, and a weighted k-means algorithm  ...  Our subtyping framework consists of a novel Time-Aware Multi-modal auto-Encoder (TAME) model which introduces time-aware attention mechanism and incorporates multi-modal inputs (e.g., demographics, diagnoses  ...  Raegan Heitzenrater for the weekly discussions of sepsis and their language editing during the preparation of the manuscript.  ... 
doi:10.1101/2020.07.26.20162214 fatcat:d3vdjpocevcpbihuvar2djadcq

EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid [article]

Yogesh Kulkarni, Sayf Hussain Z, Krithi Ramamritham, Nivethitha Somu
2021 arXiv   pre-print
This framework utilises an enhanced Dynamic Time Warping Based Imputation (eDTWBI) algorithm to impute missing values in the time series data and leverages the Near-miss undersampling technique to generate  ...  However, imbalanced data, consecutive missing values, large training times, and complex architectures hinder the real time application of electricity theft detection models.  ...  Auto-Encoder Auto-Encoder 1 Auto-Encoder 2 Auto-Encoder 3 Layers Parameters Layers Parameters Layers Parameters Layers Parameters Input (1034, ) Input (1034, ) Input (512, ) Input (  ... 
arXiv:2110.04502v1 fatcat:2ar2ergmxndmzligywy4s5w4yu

Learning Disentangled Representations of Video with Missing Data [article]

Armand Comas-Massagué, Chi Zhang, Zlatan Feric, Octavia Camps, Rose Yu
2020 arXiv   pre-print
We present Disentangled Imputed Video autoEncoder (DIVE), a deep generative model that imputes and predicts future video frames in the presence of missing data.  ...  DIVE imputes each object's trajectory where data is missing. On a moving MNIST dataset with various missing scenarios, DIVE outperforms the state of the art baselines by a substantial margin.  ...  Acknowledgments and Disclosure of Funding  ... 
arXiv:2006.13391v2 fatcat:ha6pbd7zjvczzcyvj6vo6cegue

Automatic Componentwise Boosting: An Interpretable AutoML System [article]

Stefan Coors and Daniel Schalk and Bernd Bischl and David Rügamer
2021 arXiv   pre-print
In practice, machine learning (ML) workflows require various different steps, from data preprocessing, missing value imputation, model selection, to model tuning as well as model evaluation.  ...  Most modern AutoML systems like auto-sklearn, H20-AutoML or TPOT aim for high predictive performance, thereby generating ensembles that consist almost exclusively of black-box models.  ...  The authors of this work take full responsibilities for its content.  ... 
arXiv:2109.05583v2 fatcat:pp4ykrqusbahhnro32r66f2jei

Estimating conditional density of missing values using deep Gaussian mixture model [article]

Marcin Przewięźlikowski, Marek Śmieja, Łukasz Struski
2020 arXiv   pre-print
Moreover, imputation obtained by replacing missing values using the mean vector of our model looks visually plausible.  ...  We consider the problem of estimating the conditional probability distribution of missing values given the observed ones.  ...  Acknowledgements The work of M. Śmieja  ... 
arXiv:2010.02183v2 fatcat:mowle3tuazeivpnjg46jvx6fj4

Approximate Inference with Amortised MCMC [article]

Yingzhen Li, Richard E. Turner, Qiang Liu
2017 arXiv   pre-print
Deep models trained using amortised MCMC are shown to generate realistic looking samples as well as producing diverse imputations for images with regions of missing pixels.  ...  We propose a novel approximate inference algorithm that approximates a target distribution by amortising the dynamics of a user-selected MCMC sampler.  ...  Missing Data Imputation We also consider missing data imputation with pixels missing from contiguous sections of the image, i.e. not at random.  ... 
arXiv:1702.08343v2 fatcat:t7igg5ix7bdgljvz7i6s6iwov4

Missing Data Imputation with OLS-based Autoencoder for Intelligent Manufacturing

Yanxia Wang, Kang Li, Shaojun Gan, Che Cameron
2019 IEEE transactions on industry applications  
Hence, a novel OLS (orthogonal least square)based autoencoder is proposed to generate new samples for the imputation of missing values.  ...  of the resultant model trained by the data.  ...  The complete data set is used for calculating and imputing the missing data in the incomplete samples according to different approaches.  ... 
doi:10.1109/tia.2019.2940585 fatcat:4mttmeppm5cfjeawzsgejh55cu

Missing Data Imputation in the Internet of Things Sensor Networks

Benjamin Agbo, Hussain Al Aqrabi, Richard Hill, Tariq Alsboui
2022 Future Internet  
The aim of this study is to identify efficient missing data imputation techniques that will ensure accurate calibration of sensors.  ...  To achieve this, we propose an efficient and robust imputation technique based on k-means clustering that is capable of selecting the best imputation technique for missing data imputation.  ...  19] , and Variational Auto Encoders (VAE) [20] .  ... 
doi:10.3390/fi14050143 fatcat:7hiuxojtvzaktgfaaye6e4wbqm
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