Anomaly Detection using Supervised Learning and Multiple Statistical Methods
Anomaly Detection using Supervised Learning and Multiple Statistical Methods
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DOI：
10.1109/icmla.2019.00211
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Watson Jia;R. Shukla;S. Sengupta
中科院分区：
文献类型：
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作者：
Watson Jia;R. Shukla;S. Sengupta
时间序列数据中异常或离群值的存在会对自动决策应用程序的效率产生不利影响。例如，在车辆交通流的上下文中，依赖于交通数据的各种服务可能受到异常的负面影响。提出了一种基于监督式长短期记忆（LSTM）神经网络和统计分析的自动异常检测方法。我们训练LSTM神经网络来预测非鲁棒统计特性，并将它们与鲁棒特性相结合，以确定时间序列数据中的异常。所提出的方法依赖于分割和可调参数的异常测试。我们在精确度，召回率和F测量方面衡量我们方法的有效性。在某些情况下，指标接近100%。我们还分析了异常患病率和模型不同特定参数的性能。
The presence of anomalies or outliers within time-series data can have a detrimental effect on the efficiency of automated decision-making applications. For example, in the context of vehicular traffic flow, various services reliant on traffic data may be negatively impacted by anomalies. This paper presents an automated anomaly detection method based on supervised Long-Short Term Memory (LSTM) neural network and statistical analysis. We train LSTM neural network to predict non-robust statistical properties and combine them with robust properties to determine the anomalies in time-series data. The proposed method relies on segmentation and tunable parameters for anomaly test. We measure the efficacy of our method in terms of Precision, Recall, and F-measure. The metrics approach to 100% for certain instances. We also analyzed the performance on the prevalence of anomalies and on varying specific parameters of the model.