基于PSO和外部知识的时序数据异常检测

在时间序列数据的异常检测中, 单一模型往往只提取与自身模型结构相关的时序特征, 从而容易忽略其他特征. 同时, 面对大规模的时序数据, 模型难以对时序数据的局部趋势进行建模. 为了解决这两个问题, 本文提出一种基于粒子群优化算法(particle swarm optimization, PSO)和外部知识的异常检测模型PEAD. PEAD模型以深度学习模型作为基模型, 引入快速傅里叶变换生成的外部知识来提高基模型对局部趋势的建模能力, 随后PEAD模型以Stacking集成学习的方式训练基模型, 再使用PSO算法对基模型的输出加权求和, 对加权求和后的重构数据进行异常检测, PSO算法能够让模型的最终输出共同关注时序数据的全局特征和时间特征, 丰富模型提取的时序特征, 从而提高模型的异常检测能力. 通过对6个公开数据集进行测试, 研究结果表明PEAD模型在大部分数据集上表现良好.

Anomaly Detection of Time Series Data Based on PSO and External Knowledge

In the anomaly detection of time series data, a single model often only extracts temporal features related to its model structure and thus tends to ignore other features. At the same time, facing large-scale temporal data, it is difficult for models to model local trends in temporal data. To address these two issues, this study proposes an anomaly detection model called PEAD based on particle swarm optimization (PSO) and external knowledge. The PEAD model uses a deep learning model as the base model and introduces external knowledge generated by the fast Fourier transform to improve the modeling ability of the base model for local trends. Subsequently, the PEAD model trains the base model through Stacking ensemble learning and then uses the PSO algorithm to sum the weighted output of the base model. The weighted sum of the reconstructed data is used for anomaly detection. The PSO algorithm enables the final output of the model to focus on the global and temporal features of the temporal data and enriches the temporal features extracted by the model, thereby improving its anomaly detection ability. By testing six publicly available datasets, the research results show that the PEAD model performs well on most of the datasets.