  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.
