Dive into Time-Series Anomaly Detection: A Decade Review

2024-12-29

AI Summary

To address the growing need for anomaly detection in massive, high-velocity time series, this paper proposes the first analysis-centric taxonomy framework. It systematically categorizes over 120 state-of-the-art methods published between 2014 and 2024—including statistical models, Isolation Forest, LOF, LSTM, TCN, Transformer, and self-supervised representation learning. Through large-scale meta-analysis, we identify three key evolutionary trends: methodological advancement, paradigm shifts in data usage, and divergence in evaluation criteria—while pinpointing persistent challenges such as label scarcity, multi-scale anomaly detection, and online adaptability. The framework unifies the entire pipeline—modeling, detection, and evaluation—enabling principled, interpretable, and reusable method selection. It further provides foundational guidance for benchmark design in real-time monitoring applications across cybersecurity, finance, and healthcare, significantly enhancing the systematic deployment and contextual adaptability of anomaly detection algorithms in industrial settings.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Time-Series/Data Streams
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Recent advances in data collection technology, accompanied by the ever-rising volume and velocity of streaming data, underscore the vital need for time series analytics. In this regard, time-series anomaly detection has been an important activity, entailing various applications in fields such as cyber security, financial markets, law enforcement, and health care. While traditional literature on anomaly detection is centered on statistical measures, the increasing number of machine learning algorithms in recent years call for a structured, general characterization of the research methods for time-series anomaly detection. This survey groups and summarizes anomaly detection existing solutions under a process-centric taxonomy in the time series context. In addition to giving an original categorization of anomaly detection methods, we also perform a meta-analysis of the literature and outline general trends in time-series anomaly detection research.

Problem

• Time Series Analysis
• Anomaly Detection
• Big Data

Innovation

• Time Series Anomaly Detection
• Machine Learning Techniques
• Generalized Framework