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The surge in real-time data collection across various industries hasunderscored the need for advanced anomaly detection in both univariate andmultivariate time series data. Traditional methods, while comprehensive, oftenstruggle to capture the complex interdependencies in such data. This paperintroduces TransNAS-TSAD, a novel framework that synergizes transformerarchitecture with neural architecture search (NAS), enhanced through NSGA-IIalgorithm optimization. This innovative approach effectively tackles thecomplexities of both univariate and multivariate time series, balancingcomputational efficiency with detection accuracy. Our evaluation reveals thatTransNAS-TSAD surpasses conventional anomaly detection models, demonstratingmarked improvements in diverse data scenarios. We also propose theEfficiency-Accuracy-Complexity Score (EACS) as a new metric for assessing modelperformance, emphasizing the crucial balance between accuracy and computationalresources. 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, highlightingits potential in a wide range of industry applications.

TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly Detection