Deep-Shallow Metaclassifier with Synthetic Minority Oversampling for Anomaly Detection in a Time Series

Abstract

Anomaly detection in data streams (and particularly time series) is today a vitally important task. Machine learning algorithms are a common design for achieving this goal. In particular, deep learning has, in the last decade, proven to be substantially more accurate than shallow learning in a wide variety of machine learning problems, and deep anomaly detection is very effective for point anomalies. However, deep semi-supervised contextual anomaly detection (in which anomalies within a time series are rare and none at all occur in the algorithm’s training data) is a more difficult problem. Hybrid anomaly detectors (a “normal model” followed by a comparator) are one approach to these problems, but the separate loss functions for the two components can lead to inferior performance. We investigate a novel synthetic-example oversampling technique to harmonize the two components of a hybrid system, thus improving the anomaly detector’s performance. We evaluate our algorithm on two distinct problems: identifying pipeline leaks and patient-ventilator asynchrony.

Prediction of Hippocampal Signals in Mice Using a Deep Learning Approach for Neurohybrid Technology Applications

“…Deep neural networks have profoundly revolutionized the field of information technology, leading to the development of more optimized and autonomous artificial intelligence systems [70][71][72]. The main benefits of deep learning are the ability to learn huge amounts of data and to predict complex dynamics without any model of the system behind it when only values of the observable variable are available [73][74][75].…”