TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly Detection
(2311.18061)Abstract
The surge in real-time data collection across various industries has underscored the need for advanced anomaly detection in both univariate and multivariate time series data. This paper introduces TransNAS-TSAD, a framework that synergizes the transformer architecture with neural architecture search (NAS), enhanced through NSGA-II algorithm optimization. This approach effectively tackles the complexities of time series data, balancing computational efficiency with detection accuracy. Our evaluation reveals that TransNAS-TSAD surpasses conventional anomaly detection models due to its tailored architectural adaptability and the efficient exploration of complex search spaces, leading to marked improvements in diverse data scenarios. We also introduce the Efficiency-Accuracy-Complexity Score (EACS) as a new metric for assessing model performance, emphasizing the balance between accuracy and computational resources. TransNAS-TSAD sets a new benchmark in time series anomaly detection, offering a versatile, efficient solution for complex real-world applications. This research highlights the TransNAS-TSAD potential across a wide range of industry applications and paves the way for future developments in the field.
Overview
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Introduces TransNAS-TSAD, a novel framework for time series anomaly detection using transformer architectures combined with NAS.
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Leverages the NSGA-II algorithm for multi-objective optimization to prioritize detection accuracy and computational efficiency.
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Introduces the Efficiency-Accuracy-Complexity Score (EACS) to evaluate models on both performance and resource consumption.
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Demonstrates superior performance of TransNAS-TSAD over traditional models in benchmark tests.
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Suggests further research in model generalization, real-time processing, and integration of neuro-symbolic systems.
Introduction
The ever-increasing volume of real-time data collection in various industries has highlighted the necessity for advanced methods in detecting anomalies in time series data. Traditional methods often struggle with the complexities of both univariate and multivariate series, leading to high false positive rates and missed detections. However, the introduction of deep learning, specifically transformer architectures, has marked a significant leap in this field. Transformers, with their self-attention mechanisms, are particularly promising in time series analysis due to their ability to capture complex data patterns effectively.
TransNAS-TSAD Framework
TransNAS-TSAD represents a novel approach to time series anomaly detection, combining the strengths of transformer architectures with neural architecture search (NAS) and NSGA-II algorithm optimization. This framework offers a multi-objective optimization solution that efficiently balances detection accuracy with computational efficiency. An important contribution lies in the introduction of the Efficiency-Accuracy-Complexity Score (EACS), a metric formulated to assess models by taking into account both accuracy and computational resource demands.
Methodology and Innovations
TransNAS-TSAD employs a multi-objective NAS framework using the NSGA-II algorithm, optimizing the transformer architecture to tackle the challenges of anomaly detection in multivariate time series data. The research leverages theoretical principles from evolutionary algorithms and empirical strengths of deep learning, crafting a robust framework capable of selecting high-performing architectures.
A noteworthy innovation is the advanced anomaly detection techniques incorporated into TransNAS-TSAD. The framework integrates adversarial elements, enhancing its ability to detect complex anomalies while maintaining model robustness and ensuring high relevance of the anomaly scores.
Benchmarking and Future Research
Evaluation reveals TransNAS-TSAD’s superior performance compared to conventional models across a variety of datasets. The model exhibits high F1 scores while maintaining reduced training times, demonstrating its practicality for deployment in real-world scenarios.
While TransNAS-TSAD sets new benchmarks, research opportunities persist in the realm of model generalization across diverse datasets, real-time data processing, and neuro-symbolic systems that could further enhance its adaptability.
Conclusion
TransNAS-TSAD sets forth a versatile solution for anomaly detection in time series data, offering significant improvements over existing methods. Its capability to balance performance with computational efficiency makes it suitable for a wide range of applications. The research encourages future developments in the field, with potential implications across industries.
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