← Back to Research Radar
Academic Publication Academic Publication

Deep Learning for Time Series Anomaly Detection: A Survey

369
Citations
January 31, 2025
Published Date

Research Abstract & Technology Focus

Time series anomaly detection is important for a wide range of research fields and applications, including financial markets, economics, earth sciences, manufacturing, and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, and heart palpitations, and is therefore of particular interest. The large size and complexity of patterns in time series data have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey provides a structured and comprehensive overview of state-of-the-art deep learning for time series anomaly detection. It provides a taxonomy based on anomaly detection strategies and deep learning models. Aside from describing the basic anomaly detection techniques in each category, their advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. Finally, it summarises open issues in research and challenges faced while adopting deep anomaly detection models to time series data.
learning series anomaly detection survey
Read Full Literature

Correlated Market Trend: Adaptive Learning

Bridging academia to market: The 60-day public search velocity mapping directly to the core technology of this paper. Dashed line represents 7-day moving average.

AI Semantic Synergy Context

Connecting this academic literature to real-world market discussions and products.

crossref.org › academic paper
100%
🔥

Deep Learning for Time Series Anomaly Detection: A Survey

Time series anomaly detection is important for a wide range of research fields and applications, including financial markets, economics, earth sciences, manufacturing, and healthcare. The presence ...
crossref.org › academic paper
0%

Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey

Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds ...
crossref.org › academic paper
0%

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

No description provided.
crossref.org › academic paper
0%

Enhancing intrusion detection: a hybrid machine and deep learning approach

AbstractThe volume of data transferred across communication infrastructures has recently increased due to technological advancements in cloud computing, the Internet of Things (IoT), and automobile...
crossref.org › academic paper
0%

Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations

Background/Objectives: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neurosci...

Frequently Asked Questions (FAQ)

Curated market intelligence mapped to this research.

What is the core focus of the research titled 'Deep Learning for Time Series Anomaly Detection: A Survey'?

This literature focuses on: Time series anomaly detection is important for a wide range of research fields and applications, including financial markets, economics, earth sciences, manufacturing, and healthcare. The presence of anomalies can indicate novel or unexpected even...

Are there open-source GitHub repositories related to Deep Learning for Time Series Anomaly Detection: A Survey?

Yes, open-source projects like THU-MAIC/OpenMAIC (Open Multi-Agent Interactive Classroom — Get an immersive, multi-agent learning experience in just one click) are actively building upon these concepts.

Which startups are commercializing the technology behind Deep Learning for Time Series Anomaly Detection: A Survey?

Products like Padel Chess are bringing this to market. Their focus is: Padel tactics learning app.

What other academic literature is closely related to 'Deep Learning for Time Series Anomaly Detection: A Survey'?

Yes, highly correlated activity was mapped. An entry titled 'Deep Learning for Time Series Anomaly Detection: A Survey' discusses this: Time series anomaly detection is important for a wide range of research fields and applications, including financial markets, economics, earth scie...

Cite this Market Intelligence Report

Reference our AI-mapped synergy between this research and the commercial market to instantly build authority.

Commercial Realization

Startups and Open Source tools heavily associated with the concepts explored in this paper.

Associated Media Narrative