Anomaly detection in multivariate time series data using deep ensemble models

Anomaly detection in time series data is essential for fraud detection and intrusion monitoring applications. However, it poses challenges due to data complexity and high dimensionality. Industrial applications struggle to process high-dimensional, complex data streams in real time despite existing...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:PloS one Jg. 19; H. 6; S. e0303890
Hauptverfasser: Iqbal, Amjad, Amin, Rashid, Alsubaei, Faisal S., Alzahrani, Abdulrahman
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 06.06.2024
Schlagworte:
Algorithms
Anomalies
Artificial neural networks
Biology and Life Sciences
Clustering
Complexity
Computer and Information Sciences
Data analysis
Data transmission
Datasets
Decision making
Deep Learning
Engineering and Technology
Forecasting
Forecasts and trends
Fraud
Graph neural networks
Industrial applications
Learning strategies
Long short-term memory
Machine learning
Manufacturing
Multivariate Analysis
Neural networks
Neural Networks, Computer
Physical Sciences
Real time
Recurrent neural networks
Research and Analysis Methods
Sequences
Social Sciences
Statistical analysis
Time Factors
Time series
ISSN:1932-6203, 1932-6203
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Anomaly detection in time series data is essential for fraud detection and intrusion monitoring applications. However, it poses challenges due to data complexity and high dimensionality. Industrial applications struggle to process high-dimensional, complex data streams in real time despite existing solutions. This study introduces deep ensemble models to improve traditional time series analysis and anomaly detection methods. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks effectively handle variable-length sequences and capture long-term relationships. Convolutional Neural Networks (CNNs) are also investigated, especially for univariate or multivariate time series forecasting. The Transformer, an architecture based on Artificial Neural Networks (ANN), has demonstrated promising results in various applications, including time series prediction and anomaly detection. Graph Neural Networks (GNNs) identify time series anomalies by capturing temporal connections and interdependencies between periods, leveraging the underlying graph structure of time series data. A novel feature selection approach is proposed to address challenges posed by high-dimensional data, improving anomaly detection by selecting different or more critical features from the data. This approach outperforms previous techniques in several aspects. Overall, this research introduces state-of-the-art algorithms for anomaly detection in time series data, offering advancements in real-time processing and decision-making across various industrial sectors.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
Competing Interests: The author have declared that no competing interest exit.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0303890