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 …

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 …

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 …

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 …

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

Abstract Analyzing the Stock Market is a perpetual process and hard to grasp, especially for
newcomers looking to invest in the market. This paper will be useful for novice investors to …

The operation of HVAC (heating, ventilation, and air-conditioning) accounts for a large
proportion of energy consumption in buildings. Accurate estimation of the energy demand …

Early failure detection and abnormal data reconstruction in sensor data provided by building
ventilation control systems are critical for public health. Early detection of abnormal data can …

This study investigates various methods for handling missing data in the development of
machine learning-based energy benchmarking models, evaluating their impact on training …