This paper presents an extensive empirical study on the integration of dimensionality reduction techniques with advanced unsupervised time series anomaly detection models, focusing on the MUTANT and Anomaly-Transformer models. The study involves a comprehensive evaluation across three different datasets: MSL, SMAP, and SWaT. Each dataset poses unique challenges, allowing for a robust assessment of the models' capabilities in varied contexts. The dimensionality reduction techniques examined include PCA, UMAP, Random Projection, and t-SNE, each offering distinct advantages in simplifying high-dimensional data. Our findings reveal that dimensionality reduction not only aids in reducing computational complexity but also significantly enhances anomaly detection performance in certain scenarios. Moreover, a remarkable reduction in training times was observed, with reductions by approximately 300\% and 650\% when dimensionality was halved and minimized to the lowest dimensions, respectively. This efficiency gain underscores the dual benefit of dimensionality reduction in both performance enhancement and operational efficiency. The MUTANT model exhibits notable adaptability, especially with UMAP reduction, while the Anomaly-Transformer demonstrates versatility across various reduction techniques. These insights provide a deeper understanding of the synergistic effects of dimensionality reduction and anomaly detection, contributing valuable perspectives to the field of time series analysis. The study underscores the importance of selecting appropriate dimensionality reduction strategies based on specific model requirements and dataset characteristics, paving the way for more efficient, accurate, and scalable solutions in anomaly detection.

翻译：本文对降维技术与先进的无监督时间序列异常检测模型（重点研究MUTANT和Anomaly-Transformer模型）的集成进行了广泛的实证研究。研究在三个不同数据集（MSL、SMAP和SWaT）上进行了全面评估。每个数据集均提出独特挑战，允许在不同背景下对模型能力进行稳健评估。所考察的降维技术包括PCA、UMAP、随机投影和t-SNE，每种技术在高维数据简化方面均具有独特优势。研究发现：降维不仅有助于降低计算复杂度，还能在特定场景下显著提升异常检测性能。此外，训练时间显著缩短：当维度减半时缩短约300%，降至最低维度时缩短约650%。这种效率提升凸显了降维在性能增强和运行效率方面的双重优势。MUTANT模型展现出显著适应性（特别是与UMAP降维结合时），而Anomaly-Transformer模型则表现出对不同降维技术的广泛兼容性。这些见解加深了对降维与异常检测协同效应的理解，为时间序列分析领域提供了宝贵视角。研究强调了根据具体模型需求和数据集特征选择合适降维策略的重要性，为开发更高效、更准确且可扩展的异常检测解决方案奠定了基础。