@article{oai:repo.qst.go.jp:00049099,
 author = {Janik, Miroslaw and Bossew, P. and Kurihara, Osamu and Janik, Miroslaw and Kurihara, Osamu},
 journal = {Science of The Total Environment},
 month = {Jul},
 note = {In this paper, as our first purpose, we propose the application of machine learning to reconstruct incomplete or irregularly sampled data of time series indoor radon ( 222 Rn). The physical assumption underlying the modelling is that Rn concentration in the air is controlled by environmental variables such as air temperature and pressure. The algorithms “learn” from complete sections of multivariate series, derive a dependence model and apply it to sections where the controls are available, but not the response (Rn), and in this way complete the Rn series. Three machine learning techniques are applied in this study, namely random forest, its extension called the gradient boosting machine 
and deep learning. For a comparison, we apply the classical multiple regression in a generalized linear model version. Performance of the models is evaluated through different metrics. The performance of the gradient boosting machine is found to be superior to that of the other techniques.},
 pages = {1155--1167},
 title = {Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data},
 volume = {630},
 year = {2018}
}