[HTML][HTML] An active learning framework for the low-frequency Non-Intrusive Load Monitoring problem
T Todic, V Stankovic, L Stankovic - Applied Energy, 2023 - Elsevier
With the widespread deployment of smart meters worldwide, quantification of energy used
by individual appliances via Non-Intrusive Load Monitoring (NILM), ie, virtual submetering, is …
by individual appliances via Non-Intrusive Load Monitoring (NILM), ie, virtual submetering, is …
A Survey of Time Series Anomaly Detection Methods in the AIOps Domain
Internet-based services have seen remarkable success, generating vast amounts of
monitored key performance indicators (KPIs) as univariate or multivariate time series …
monitored key performance indicators (KPIs) as univariate or multivariate time series …
Runtime Performance Anomaly Diagnosis in Production HPC Systems Using Active Learning
With the increasing scale and complexity of High-Performance Computing (HPC) systems,
performance variations in applications caused by anomalies have become significant …
performance variations in applications caused by anomalies have become significant …
[HTML][HTML] AMAD: Active learning-based multivariate time series anomaly detection for large-scale IT systems
R Yu, Y Wang, W Wang - Computers & Security, 2024 - Elsevier
Multivariate time series anomaly detection on key performance indicators helps mitigate the
impact of large-scale IT system anomalies. Due to the large volume and the abstract nature …
impact of large-scale IT system anomalies. Due to the large volume and the abstract nature …
[HTML][HTML] Human in the loop active learning for time-series electrical measurement data
Advanced machine learning algorithms require large datasets, along with good-quality
labels to reach state-of-the-art performance. Although measurements themselves can often …
labels to reach state-of-the-art performance. Although measurements themselves can often …
Active Learning for Data Quality Control: A Survey
Data quality plays a vital role in scientific research and decision-making across industries.
Thus it is crucial to incorporate the data quality control (DQC) process, which comprises …
Thus it is crucial to incorporate the data quality control (DQC) process, which comprises …
Detection Latencies of Anomaly Detectors: An Overlooked Perspective?
T Puccetti, A Ceccarelli - arXiv preprint arXiv:2402.09082, 2024 - arxiv.org
The ever-evolving landscape of attacks, coupled with the growing complexity of ICT systems,
makes crafting anomaly-based intrusion detectors (ID) and error detectors (ED) a difficult …
makes crafting anomaly-based intrusion detectors (ID) and error detectors (ED) a difficult …
Prolego: Time-Series Analysis for Predicting Failures in Complex Systems
A Das, A Aiken - … Conference on Autonomic Computing and Self …, 2023 - ieeexplore.ieee.org
Failures in large, complex systems can be difficult to diagnose and expensive for both the
system maintainers and users. We present techniques for predicting failures when there is …
system maintainers and users. We present techniques for predicting failures when there is …
An interactive threshold-setting procedure for improved multivariate anomaly detection in time series
A Lundström, M O'Nils, FZ Qureshi - IEEE Access, 2023 - ieeexplore.ieee.org
Anomaly detection in multivariate time series is valuable for many applications. In this
context, unsupervised and semi-supervised deep learning methods that estimate how …
context, unsupervised and semi-supervised deep learning methods that estimate how …
SNN-AAD: active anomaly detection method for multivariate time series with sparse neural network
Anomaly detection of time series data is an important and popular problem in both research
and application fields. Kinds of solutions have been developed to uncover the anomaly …
and application fields. Kinds of solutions have been developed to uncover the anomaly …