Long Gaps Missing IoT Sensors Time Series Data Imputation: A Bayesian Gaussian Approach-Reference-Cited by-同舟云学术

Long Gaps Missing IoT Sensors Time Series Data Imputation: A Bayesian Gaussian Approach

Published:2022 Issue: Volume:10 Page:116107-116119
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ahmed Hassan M.¹^ORCID,Abdulrazak Bessam¹^ORCID,Blanchet F. Guillaume²,Aloulou Hamdi³,Mokhtari Mounir⁴

Affiliation:

1. Département d’informatique, Faculté des sciences, Ambient Intelligence Laboratory (AMI-Lab), Université de Sherbrooke, Sherbrooke, Canada

2. Département de Biologie, Faculté des sciences, Université de Sherbrooke, Sherbrooke, Canada

3. ReDCAD, ENIS, University of Sfax, Sfax, Tunisia

4. Institut Mines Télécom, Paris, France

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09934837.pdf?arnumber=9934837

Reference36 articles.

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2. Data Estimation in Sensor Networks Using Physical and Statistical Methodologies

3. A spatial–temporal imputation technique for classification with missing data in a wireless sensor network;li;Proc IEEE/RSJ Int Conf Intell Robots Syst,2008

4. Imputation of missing value using dynamic Bayesian network for multivariate time series data

5. Dynamic linear models

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