This study explores the applicability of data augmentation techniques for reconstructing
missing energy time-series in limited data regimes. In particular, multiple synthetic copies of …

This study explored methods for handling missing data in the development of machine
learning-based energy benchmarking models, assessing their training time, performance …

Due to signal loss and instrument failure, the arch dam monitoring system fails to obtain a
complete deformation monitoring sequence. Therefore, the framework to fill in missing …

Missing data are frequently observed by practitioners and researchers in the building energy
modeling community. In this regard, advanced data-driven solutions, such as Deep Learning …

Increasing advancements in building digitization, smart sensing, and metering technologies
have allowed large amounts of timeseries data to be collected for monitoring, analyzing, and …

Behavioral prediction modeling applies statistical techniques for classifying, recognizing,
and predicting behavior using various data. However, performance deterioration and data …

The use of data is essential for the supply of business, scientific and other processes. Often
the consumption of these data is hampered when there are sample losses. Aiming to …

Data missing and anomalies in monitoring equipment have become critical barriers to
develo** intelligent Water Supply Systems (WSS). The valid data preceding and after the …

Accurate and comprehensive air pollution data is essential for understanding and
addressing environmental challenges. Missing data can impair accurate analysis and …

As the scale of online news and social media expands, attempts to analyze the latest social
issues and consumer trends are increasing. Research on detecting spatio-temporal event …