Dive into Time-Series Anomaly Detection: A Decade Review (2412.20512v1)

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.

Dive into Time-Series Anomaly Detection: A Decade Review

The paper "Dive into Time-Series Anomaly Detection: A Decade Review" provides a comprehensive overview of the research landscape surrounding anomaly detection in time-series data. It systematically categorizes and examines the diverse methodologies that have emerged over the past ten years, highlighting the evolution from traditional statistical approaches to modern machine learning-based techniques. This paper is a crucial reference for researchers interested in understanding the current state and future directions of time-series anomaly detection.

Anomaly Detection in Time Series

Time-series anomaly detection involves identifying data points or sequences that deviate significantly from expected patterns. This task is essential in various applications such as cybersecurity, finance, healthcare, and engineering. Historically, anomaly detection in time series has been approached through statistical methods focused on defining thresholds based on distribution characteristics. However, with the advent of large-scale data and advanced computational capabilities, machine learning has become a dominant framework in tackling this challenge.

Taxonomy and Methodological Approaches

The survey introduces a process-centric taxonomy to organize existing methods, dividing them into three main categories: distance-based, density-based, and prediction-based. These categories are chosen to reflect the methodological focus of these approaches and are further divided into sub-categories to facilitate a detailed understanding of each method's unique characteristics.

1. Distance-based Methods: These methods utilize distance metrics to identify outliers, such as Euclidean distance or dynamic time warping. They include proximity-based approaches like K-Nearest Neighbors (KNN) and Local Outlier Factor (LOF), clustering-based methods such as k-means and DBSCAN, and discord-based methodologies, including various implementations of the Matrix Profile.
2. Density-based Methods: This category involves constructing a representation of the data to identify deviations from the norm, like graph-based or distribution-based approaches. Techniques range from complex models like Hidden Markov Models to simpler histogram-based methods, offering versatility depending on data characteristics and computational resources.
3. Prediction-based Methods: These methods focus on predicting future time-series values and identifying anomalies based on prediction errors. This category includes a range of techniques from traditional ARIMA models to cutting-edge deep learning architectures such as Long Short-Term Memory (LSTM) and Generative Adversarial Networks (GANs).

Key Findings and Trends

The survey identifies a significant shift towards machine learning paradigms for anomaly detection in time series, driven by the availability of computational resources and the ability to model complex patterns in data. The most notable trend is the integration of deep learning techniques, which allow for the automatic extraction of features and adaptive learning of anomalies without predefined thresholds. This shift is supported by the increasing availability of annotated datasets and benchmarks like NAB and TSB-AD, providing standardized evaluation frameworks.

Implications and Future Directions

The findings of this survey underscore the importance of selecting appropriate methods based on the characteristics of the data and the specific application requirements. The paper advocates for more comprehensive benchmarking and standardized evaluation metrics to facilitate fair comparisons between different approaches. Furthermore, there is an urgent need for methods that handle the intricacies of modern data, such as multivariate time series, streaming data, and missing data. The paper suggests that future research could focus on hybrid models that combine the strengths of various techniques and improve the interpretability of machine learning models in this context.

In conclusion, this review paper presents an invaluable resource that synthesizes a decade of research into a coherent narrative, charting the taxonomy of tools and methodologies available to practitioners. Researchers and practitioners can leverage this survey to navigate the complex landscape of time-series anomaly detection, select appropriate techniques, and push forward the boundaries of what is achievable in anomaly detection.