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. Traditional methods, while comprehensive, often struggle to capture the complex interdependencies in such data. This paper introduces TransNAS-TSAD, a novel framework that synergizes transformer architecture with neural architecture search (NAS), enhanced through NSGA-II algorithm optimization. This innovative approach effectively tackles the complexities of both univariate and multivariate time series, balancing computational efficiency with detection accuracy. Our evaluation reveals that TransNAS-TSAD surpasses conventional anomaly detection models, demonstrating marked improvements in diverse data scenarios. We also propose the Efficiency-Accuracy-Complexity Score (EACS) as a new metric for assessing model performance, emphasizing the crucial 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 paves the way for future developments in the field, highlighting its potential in a wide range of industry applications.

翻译：各行业实时数据采集的激增凸显了在单变量和多变量时间序列数据中进行高级异常检测的必要性。传统方法虽全面，但往往难以捕捉此类数据中的复杂相互依赖关系。本文提出TransNAS-TSAD这一新型框架，该框架将Transformer架构与神经架构搜索（NAS）协同融合，并通过NSGA-II算法优化增强。这一创新方法有效应对了单变量和多变量时间序列的复杂性，在计算效率与检测精度之间取得平衡。我们的评估表明，TransNAS-TSAD超越了传统异常检测模型，在不同数据场景中展现出显著改进。我们还提出效率-精度-复杂度评分（EACS）作为评估模型性能的新指标，强调精度与计算资源之间的关键平衡。TransNAS-TSAD为时间序列异常检测树立了新基准，为复杂的实际应用提供了一种通用、高效的解决方案。本研究为该领域的未来发展铺平了道路，凸显其在广泛行业应用中的潜力。