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.

翻译：各行业实时数据采集的激增凸显了对单变量及多变量时间序列数据进行高级异常检测的迫切需求。本文提出TransNAS-TSAD框架，该框架将Transformer架构与神经架构搜索（NAS）相结合，并通过NSGA-II算法优化进行增强。该方法有效应对时间序列数据的复杂性，在计算效率与检测精度之间取得平衡。实验评估表明，TransNAS-TSAD凭借其定制化的架构适应性以及对复杂搜索空间的高效探索，显著优于传统异常检测模型，在不同数据场景中均实现了性能提升。我们还引入效率-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权衡。TransNAS-TSAD为时间序列异常检测树立了新标杆，为复杂现实应用提供了多功能、高效的解决方案。本研究凸显了TransNAS-TSAD在广泛工业应用中的潜力，并为该领域的未来研究奠定了基础。