  In the process of collecting data from sensors, several circumstances can
affect their continuity and validity, resulting in alterations of the data or
loss of information. Although classical methods of statistics, such as
interpolation-like techniques, can be used to approximate the missing data in a
time series, the recent developments in Deep Learning (DL) have given impetus
to innovative and much more accurate forecasting techniques. In the present
paper, we develop two DL models aimed at filling data gaps, for the specific
case of internal temperature time series obtained from monitored apartments
located in Bolzano, Italy. The DL models developed in the present work are
based on the combination of Convolutional Neural Networks (CNNs), Long
Short-Term Memory Neural Networks (LSTMs), and Bidirectional LSTMs (BiLSTMs).
Two key features of our models are the use of both pre- and post-gap data, and
the exploitation of a correlated time series (the external temperature) in
order to predict the target one (the internal temperature). Our approach
manages to capture the fluctuating nature of the data and shows good accuracy
in reconstructing the target time series. In addition, our models significantly
improve the already good results from another DL architecture that is used as a
baseline for the present work.
