2026-05-09T11:22:11.905Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:2:8924 2022-06-17T09:47:13Z 2

Imputing missing indoor air quality data with inverse mapping generative adversarial network 2022 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/8924 https://www.sciencedirect.com/science/article/abs/pii/S036013232200141X Z. Wu M. Chao X. Shi L. Wu Y. Dong M. Stojmenović Sensors deployed all over the buildings are nowadays collecting a large amount of data, such as the Indoor Air Quality (IAQ) data which can provide valuable suggestions on improving indoor environments and energy consumption strategies. However, as treated as Multivariate Time Series (MTS), IAQ data often contain missing values that severely limit further analysis on them. Unfortunately, most of the existing methods fail to handle a couple of technical issues due to the complexity of MTS data, such as data distribution approximation, removing the redundancy, and so on. In this paper, we formulate the IAQ missing data imputation problem and propose an Inverse Mapping Generative Adversarial Network (IM-GAN) to tackle that problem. article 215 108896 108899 0360-1323 BUILDING AND ENVIRONMENT