优化超参数提升Bi-LSTM在智能家电异常检测中的表现
Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector
- 用离线优化方法自动调优Bi-LSTM超参数，再用于在线检测
- 在两个空气质量数据集上，F1最高达0.92，召回率超0.85
- 适合做物联网场景下需实时响应的上下文异常检测
物联网在日常生活的广泛应用导致时间序列数据急剧增长。智能家居是其中数据生成量大的领域之一，异常检测是近年研究的重要挑战。上下文异常指行为偏离正常模式（如点或序列异常），但需结合领域知识判断其合理性。近年来基于循环神经网络（RNN）的方法在异常检测中表现优异。本研究探索了自动调优超参数对无监督在线上下文异常检测（UoCAD）的影响，提出改进方法UoCAD-OH。该方法在离线阶段对Bi-LSTM模型进行超参数优化，并将最优参数用于在线异常检测。实验在包含上下文异常的两个智能家居空气质量数据集上评估，使用精确率、召回率和F1分数作为评价指标。结果表明，优化后模型性能显著提升。
原文摘要 · Abstract (English)
The exponential growth in the usage of Internet of Things in daily life has caused immense increase in the generation of time series data. Smart homes is one such domain where bulk of data is being generated and anomaly detection is one of the many challenges addressed by researchers in recent years. Contextual anomaly is a kind of anomaly that may show deviation from the normal pattern like point or sequence anomalies, but it also requires prior knowledge about the data domain and the actions that caused the deviation. Recent studies based on Recurrent Neural Networks (RNN) have demonstrated strong performance in anomaly detection. This study explores the impact of automatically tuned hyperparamteres on Unsupervised Online Contextual Anomaly Detection (UoCAD) approach by proposing UoCAD with Optimised Hyperparamnters (UoCAD-OH). UoCAD-OH conducts hyperparameter optimisation on Bi-LSTM model in an offline phase and uses the fine-tuned hyperparameters to detect anomalies during the online phase. The experiments involve evaluating the proposed framework on two smart home air quality datasets containing contextual anomalies. The evaluation metrics used are Precision, Recall, and F1 score.
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