A Multivariate-Time-Series-Prediction-Based Adaptive Data Transmission Period Control Algorithm for IoT Networks

Natural climate reconstruction in the Norwegian stave churches through time series processing with variational autoencoders

“…In general, time-series forecasting and reconstruction is a task common to many fields, such as IoT (Han et al, 2021), solar physics (Arslan and Sekertekin, 2019), building energy minimization (Liguori et al, 2021) and cultural heritage (Bertolin et al, 2015). In literature, three main different approaches are proposed: missing data are reconstructed through parametric models based on a priori knowledge about the system (American Society of Heating Refrigerating and Air-Conditioning Engineers, 2011; Ciulla et al, 2010;Giaconia and Orioli, 2000), through data-driven approaches (such as deep learning) (Arslan and Sekertekin, 2019;Liguori et al, 2021), or through a mixture of the two (Han et al, 2021).…”

“…The third approach (Han et al, 2021), through an initial pre-processing and augmentation of the data, makes it possible to build a sufficiently large dataset to feed a machine learning algorithm able to model the system, including its stochastic parts. In this way, good use is made of both clean and spoiled data, which still carry meaningful information and should not thus be simply discarded.…”

“…The current work is based on reconstructing the whole natural climate time-series of a wooden heritage building located in Norway: the Ringebu Stave Church. Based on the literature framework, the existing methods seem to be not directly applicable to the current case study as they mainly focus on one-step-ahead predictions (Arslan and Sekertekin, 2019;Han et al, 2021) or short intervals reconstruction (less than 20 h) (Liguori et al, 2021). In the case of this work, instead, the measurements cover one-year period and data gaps that last some days, up to a week, are Natural climate reconstruction in churches present as well.…”

“…In general, time-series forecasting and reconstruction is a task common to many fields, such as IoT (Han et al. , 2021), solar physics (Arslan and Sekertekin, 2019), building energy minimization (Liguori et al.…”

“…, 2010; Giaconia and Orioli, 2000), through data-driven approaches (such as deep learning) (Arslan and Sekertekin, 2019; Liguori et al. , 2021), or through a mixture of the two (Han et al. , 2021).…”