We investigate anomaly detection in an unsupervised framework and introduce long short-
term memory (LSTM) neural network-based algorithms. In particular, given variable length …

We explore the use of Long short-term memory (LSTM) for anomaly detection in temporal
data. Due to the challenges in obtaining labeled anomaly datasets, an unsupervised …

To address one of the most challenging industry problems, we develop an enhanced
training algorithm for anomaly detection in unlabelled sequential data such as time-series …

This abstract proposes a time series anomaly detector which 1) makes no assumption about
the underlying mechanism of anomaly patterns, 2) refrains from the cumbersome work of …

Automatic anomaly detection in data mining has a wide range of applications such as fraud
detection, system health monitoring, fault detection, event detection systems in sensor …

Anomalies in time series, also called “discord,” are the abnormal subsequences. The
occurrence of anomalies in time series may indicate that some faults or disease will occur …

Support Vector Machines (SVMs) have been one of the most successful machine learning
techniques for the past decade. For anomaly detection, also a semi-supervised variant, the …

High-dimensional problem domains pose significant challenges for anomaly detection. The
presence of irrelevant features can conceal the presence of anomalies. This problem, known …

Anomaly detection is a classical but worthwhile problem, and many deep learning-based
anomaly detection algorithms have been proposed, which can usually achieve better …

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