面向多元时间序列异常检测的高效深度自编码器
Towards efficient deep autoencoders for multivariate time series anomaly detection
AI总结：
针对深度自编码器在实时多元时间序列异常检测中的资源约束问题，提出结合剪枝与线性、非线性量化的压缩方法，在保持检测性能的同时实现80%至95%的模型压缩。
摘要：
多元时间序列异常检测是许多工业和研究应用中的一个关键问题。例如，及时检测异常能够预防制造过程中的缺陷以及信息物理系统中的故障。在分析复杂多元数据时，深度学习方法因其准确性和鲁棒性而优于其他方法。然而，一个关键方面是能够及时给出预测，以满足不同应用中的实时需求。对于深度学习模型而言，模型压缩对于在时间和内存受限的实时系统中取得最佳结果极为重要。本文中，我们通过提出一种新颖的深度自编码器压缩方法来解决这一问题，该方法涉及三个关键因素。首先，剪枝减少权重数量，同时通过识别高稀疏度水平的快速搜索过程防止准确率灾难性下降。其次，线性和非线性量化通过减少每个权重的位数来降低模型复杂度。这三个方面的共同作用使模型尺寸得以减小，即移除一部分权重（剪枝）并降低其位宽（量化）。因此，压缩后的模型速度更快，也更易于在高度受限的硬件环境中部署。在常用的多元异常检测基准上进行的实验表明，我们的方法能够实现显著的模型压缩率（介于80%和95%之间），且不会显著降低异常检测性能。
英文摘要：
Multivariate time series anomaly detection is a crucial problem in many industrial and research applications. Timely detection of anomalies allows, for instance, to prevent defects in manufacturing processes and failures in cyberphysical systems. Deep learning methods are preferred among others for their accuracy and robustness for the analysis of complex multivariate data. However, a key aspect is being able to extract predictions in a timely manner, to accommodate real-time requirements in different applications. In the case of deep learning models, model reduction is extremely important to achieve optimal results in real-time systems with limited time and memory constraints. In this paper, we address this issue by proposing a novel compression method for deep autoencoders that involves three key factors. First, pruning reduces the number of weights, while preventing catastrophic drops in accuracy by means of a fast search process that identifies high sparsity levels. Second, linear and non-linear quantization reduces model complexity by reducing the number of bits for every single weight. The combined contribution of these three aspects allow the model size to be reduced, by removing a subset of the weights (pruning), and decreasing their bit-width (quantization). As a result, the compressed model is faster and easier to adopt in highly constrained hardware environments. Experiments performed on popular multivariate anomaly detection benchmarks, show that our method is capable of achieving significant model compression ratio (between 80% and 95%) without a significant reduction in the anomaly detection performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
