LSTM-Autoencoder-Based Anomaly Detection for Indoor Air Quality Time-Series Data-Reference-Cited by-同舟云学术

LSTM-Autoencoder-Based Anomaly Detection for Indoor Air Quality Time-Series Data

Published:2023-02-15 Issue:4 Volume:23 Page:3787-3800
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Wei Yuanyuan¹^ORCID,Jang-Jaccard Julian¹^ORCID,Xu Wen¹^ORCID,Sabrina Fariza²^ORCID,Camtepe Seyit³^ORCID,Boulic Mikael⁴^ORCID

Affiliation:

1. Cybersecurity Laboratory, Computer Science and Information Technology, Massey University, Auckland, New Zealand

2. School of Engineering and Technology, Central Queensland University, Sydney, NSW, Australia

3. CSIRO Data61, Eveleigh, NSW, Australia

4. School of Built Environment, Massey University, Auckland, New Zealand

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10043099/10011213.pdf?arnumber=10011213

Reference31 articles.

1. IndoAirSense: A framework for indoor air quality estimation and forecasting

2. Variational LSTM Enhanced Anomaly Detection for Industrial Big Data

3. Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective

4. An Internet of Things framework to forecast indoor air quality using machine learning;rastogi;Proc Symp Mach Learn Metaheuristics Algorithms Appl,2019

5. An anomaly detection approach based on the combination of LSTM autoencoder and isolation forest for multivariate time series data

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