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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.<\/jats:p>","DOI":"10.1186\/s40537-023-00746-z","type":"journal-article","created":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T07:02:31Z","timestamp":1684306951000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Unsupervised outlier detection for time-series data of indoor air quality using LSTM autoencoder with ensemble method"],"prefix":"10.1186","volume":"10","author":[{"given":"Junhyeok","family":"Park","sequence":"first","affiliation":[]},{"given":"Youngsuk","family":"Seo","sequence":"additional","affiliation":[]},{"given":"Jaehyuk","family":"Cho","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,17]]},"reference":[{"key":"746_CR1","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1016\/j.eswa.2016.03.029","volume":"57","author":"D Zheng","year":"2016","unstructured":"Zheng D, Li F, Zhao T. 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