Unsupervised outlier detection for time-series data of indoor air quality using LSTM autoencoder with ensemble method

Autor:	Junhyeok Park, Youngsuk Seo, Jaehyuk Cho
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	Long short-term memory autoencoder Environmental sensor Anomaly detection Multivariate time series Condition monitoring Computer engineering. Computer hardware TK7885-7895 Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
Zdroj:	Journal of Big Data, Vol 10, Iss 1, Pp 1-24 (2023)
Druh dokumentu:	article
ISSN:	2196-1115
DOI:	10.1186/s40537-023-00746-z
Popis:	Abstract The proposed framework consists of three modules as an outlier detection method for indoor air quality data. We first use a long short-term memory autoencoder (LSTM-AE) based reconstruction error detector, which designs the LSTM layer in the shape of an autoencoder, to build a reconstruction error-based outlier detection model and extract latent features. The latent feature class-assisted vector machine detector constructs an additional outlier detection model using previously extracted latent features. Finally, the ensemble detector combines the two independent classifiers to define a new ensemble-based decision rule. Furthermore, because real-time anomaly detection proceeds with unsupervised learning, more stable and consistent external detection rules are defined than when using a single ensemble model. Laboratory tests with five random cases were performed for objective evaluation. Thus, we propose a framework that can be applied to various industrial environments by detecting and defining stable outlier decision rules.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/76eec06160e34f84a66ffc892f2ce75f Zobrazit plný text záznamu Full text from SpringerLink View record in DOAJ