As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …

Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

With ongoing automation and digitization of the electric power system, several Phasor
Measurement Units (PMUs) have been deployed for monitoring and control. PMU data can …

Feature-based time series representations have attracted substantial attention in a wide
range of time series analysis methods. Recently, the use of time series features for forecast …

The HDoutliers algorithm is a powerful unsupervised algorithm for detecting anomalies in
high-dimensional data, with a strong theoretical foundation. However, it suffers from some …

Data quality significantly impacts the results of data analytics. Researchers have proposed
machine learning based anomaly detection techniques to identify incorrect data. Existing …

This paper demonstrates that the performance of various outlier detection methods is
sensitive to both the characteristics of the dataset, and the data normalization scheme …

The efficient operation of industrial robots relies on reliable anomaly detection systems, but
the problem of missing data caused by sensor failures, data transmission errors, and system …

This study proposes a control chart based on functional data to detect anomalies and
estimate the normal output of industrial processes and services such as those related to the …

The challenge of efficiently identifying anomalies in data sequences is an important
statistical problem that now arises in many applications. Although there has been substantial …