TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models
TimeSeriesBench offers 168 evaluation settings to enhance the industrial applicability of time series anomaly detection models.
Key Findings
Methodology
TimeSeriesBench integrates existing evaluation standards and introduces event-driven metrics, providing an industrial-grade benchmark. It uses All-in-One and Zero-Shot paradigms to evaluate various algorithms on unified models and new time series, covering statistical and deep learning methods.
Key Results
- In 168 evaluation settings, variational autoencoders excel in pattern anomaly detection, while general time series models struggle to outperform task-specific methods.
- Experiments show that unified models trained in All-in-One mode achieve high detection accuracy across multiple datasets, especially AIOPS and UCR.
- In Zero-Shot mode, models maintain high detection performance on new time series, demonstrating good generalization.
Significance
TimeSeriesBench provides a comprehensive evaluation framework for time series anomaly detection, addressing issues of high maintenance costs, inability to handle new time series, and impractical evaluation standards. It offers practical guidance for future algorithm design, bridging academia and industry.
Technical Contribution
This study introduces All-in-One and Zero-Shot evaluation modes for the first time, significantly reducing model maintenance costs and proposing event-driven metrics to enhance practical guidance.
Novelty
TimeSeriesBench is the first benchmark to apply All-in-One and Zero-Shot modes to time series anomaly detection, offering more practical evaluation methods.
Limitations
- The current framework mainly targets univariate time series, which may not apply to multivariate scenarios.
- Some datasets may have inaccurate anomaly labels, affecting the reliability of evaluation results.
Future Work
Future research can expand to multivariate time series anomaly detection, explore more complex evaluation standards, and develop more efficient unified models.
AI Executive Summary
Time series anomaly detection is crucial in modern software systems, but existing algorithms' practicality in large-scale systems and detection capabilities on new time series remain unclear. TimeSeriesBench introduces All-in-One and Zero-Shot modes, providing an industrial-grade benchmark to evaluate various algorithms across 168 settings.
The platform integrates existing evaluation standards and proposes event-driven metrics to ensure effective guidance for industrial applications. Experimental results show that variational autoencoders excel in pattern anomaly detection, while general time series models need improvement in specific tasks.
The release of TimeSeriesBench offers a comprehensive evaluation framework for time series anomaly detection, addressing issues of high maintenance costs, inability to handle new time series, and impractical evaluation standards, bridging academia and industry.
Deep Analysis
Background
Time series anomaly detection is highly valued in both industry and academia for its application in improving software system stability. In recent years, deep learning-based anomaly detection methods have proliferated, but results vary significantly across papers due to inconsistent evaluation standards, making practical guidance for industrial applications difficult.
Core Problem
Existing algorithms typically train a specific model for each time series, which is impractical for large-scale systems. Additionally, frequent deployments and upgrades in distributed systems lead to new time series, and current algorithms' performance on these is unknown.
Innovation
TimeSeriesBench introduces All-in-One and Zero-Shot modes to address high maintenance costs in large-scale systems and proposes event-driven metrics to ensure practical guidance for industrial applications.
Methodology
- �� All-in-One mode: Train a unified model to evaluate its detection performance across multiple time series.
- �� Zero-Shot mode: Use a novel data-splitting method to evaluate model performance on unseen time series.
- �� Event-driven evaluation metrics: Integrate existing standards and propose new evaluation methods.
Experiments
Experiments use datasets like AIOPS, WSD, Yahoo, NAB, and UCR to evaluate statistical and deep learning methods under different modes. By comparing various evaluation standards, the effectiveness of new methods is verified.
Results
In 168 evaluation settings, variational autoencoders excel in pattern anomaly detection, while general time series models need improvement. Unified models in All-in-One mode achieve high detection accuracy across multiple datasets.
Applications
TimeSeriesBench can be used to evaluate and optimize time series anomaly detection algorithms, particularly in large-scale industrial systems. Its evaluation results provide practical guidance for algorithm selection and improvement.
Limitations & Outlook
The current framework mainly targets univariate time series, which may not apply to multivariate scenarios. Some datasets may have inaccurate anomaly labels, affecting the reliability of evaluation results. Future research can expand to multivariate time series anomaly detection.
Plain Language Accessible to non-experts
Imagine you're in a massive factory responsible for monitoring all the machines. Each machine's operation is recorded as a timeline, and any anomaly could cause production issues. TimeSeriesBench is like a super-intelligent supervisor that can monitor all machines simultaneously and alert you immediately when it detects an anomaly. It can handle data from old machines and quickly adapt to new ones, ensuring the factory runs efficiently and reduces downtime losses.
ELI14 Explained like you're 14
Imagine you're playing a massive multiplayer online game, and the game server needs to constantly monitor players' activities to prevent cheating or server crashes. TimeSeriesBench is like a game admin bot that can monitor thousands of players' activities at once and take action immediately when it detects something unusual. Even when new players join, it quickly adapts to their playstyle, ensuring a fair and stable gaming environment.
Glossary
Time Series
A sequence of data points ordered in time, typically used to represent the change of a metric over time.
Used to monitor system performance metrics like response time or success rate.
Anomaly Detection
Identifying points or patterns in data that deviate from the norm, often indicating potential issues.
Used to detect system failures or network attacks.
Variational Autoencoder
A generative model that learns the probability distribution of data to generate new data points.
Used for detecting pattern anomalies in time series.
All-in-One Mode
A training mode using a single unified model for multiple time series, reducing maintenance costs.
Evaluates model performance across multiple time series.
Zero-Shot Mode
An evaluation mode testing model performance on unseen time series, assessing generalization ability.
Used to evaluate model performance on new time series.
Open Questions Unanswered questions from this research
- 1 How to apply TimeSeriesBench to multivariate time series? Current methods mainly target univariate scenarios, requiring expansion to more complex applications.
- 2 How to improve dataset labeling accuracy? Some datasets may have inaccurate anomaly labels, affecting the reliability of evaluation results.
Applications
Immediate Applications
Industrial System Monitoring
TimeSeriesBench can be used to evaluate and optimize anomaly detection algorithms in industrial systems, helping companies improve system stability.
Long-term Vision
Smart City Management
By monitoring the operational status of urban infrastructure in real-time, TimeSeriesBench can be used for anomaly detection in smart cities, enhancing city management efficiency.
Abstract
Time series anomaly detection (TSAD) has gained significant attention due to its real-world applications to improve the stability of modern software systems. However, there is no effective way to verify whether they can meet the requirements for real-world deployment. Firstly, current algorithms typically train a specific model for each time series. Maintaining such many models is impractical in a large-scale system with tens of thousands of curves. The performance of using merely one unified model to detect anomalies remains unknown. Secondly, most TSAD models are trained on the historical part of a time series and are tested on its future segment. In distributed systems, however, there are frequent system deployments and upgrades, with new, previously unseen time series emerging daily. The performance of testing newly incoming unseen time series on current TSAD algorithms remains unknown. Lastly, the assumptions of the evaluation metrics in existing benchmarks are far from practical demands. To solve the above-mentioned problems, we propose an industrial-grade benchmark TimeSeriesBench. We assess the performance of existing algorithms across more than 168 evaluation settings and provide comprehensive analysis for the future design of anomaly detection algorithms. An industrial dataset is also released along with TimeSeriesBench.