Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges
Anomaly detection has recently been applied to various areas, and several techniques
based on deep learning have been proposed for the analysis of multivariate time series. In …
based on deep learning have been proposed for the analysis of multivariate time series. In …
A review on outlier/anomaly detection in time series data
Recent advances in technology have brought major breakthroughs in data collection,
enabling a large amount of data to be gathered over time and thus generating time series …
enabling a large amount of data to be gathered over time and thus generating time series …
Applications of shapelet transform to time series classification of earthquake, wind and wave data
Autonomous detection of desired events from large databases using time series
classification is becoming increasingly important in civil engineering as a result of continued …
classification is becoming increasingly important in civil engineering as a result of continued …
ConvTrans-CL: Ocean time series temperature data anomaly detection based context contrast learning
Ocean temperature data anomaly detection is instrumental in monitoring environmental
changes and implementing measures to alleviate adverse consequences. This holds …
changes and implementing measures to alleviate adverse consequences. This holds …
Time-series anomaly detection using dynamic programming based longest common subsequence on sensor data
This study proposes a novel approach to time-series anomaly detection by solving the
longest common subsequence (LCS) problem of two time-series data. The conventional …
longest common subsequence (LCS) problem of two time-series data. The conventional …
Graph embedding-based Anomaly localization for HVAC system
As a major energy consumption system in buildings, anomaly detection on multivariate time
series monitored by sensors in HVAC systems has been a significant challenge. However …
series monitored by sensors in HVAC systems has been a significant challenge. However …
A new distributional treatment for time series anomaly detection
Time series is traditionally treated with two main approaches, ie, the time domain approach
and the frequency domain approach. These approaches must rely on a sliding window so …
and the frequency domain approach. These approaches must rely on a sliding window so …
A one-class Shapelet dictionary learning method for wind turbine bearing anomaly detection
Detecting main shaft bearing anomaly is crucial to ensure the safe operation of wind
turbines. However, existing anomaly detection methods have a limitation that anomaly …
turbines. However, existing anomaly detection methods have a limitation that anomaly …
[HTML][HTML] Outlier detection for multivariate time series: A functional data approach
Á López-Oriona, JA Vilar - Knowledge-Based Systems, 2021 - Elsevier
A method for detecting outlier samples in a multivariate time series dataset is proposed. It is
assumed that an outlying series is characterized by having been generated from a different …
assumed that an outlying series is characterized by having been generated from a different …
A new measure between sets of probability distributions with applications to erratic financial behavior
This paper introduces a new framework to quantify distance between finite sets with
uncertainty present, where probability distributions determine the locations of individual …
uncertainty present, where probability distributions determine the locations of individual …