Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector
This work addresses anomaly detection for smart home systems, but it is incremental as it focuses on hyperparameter tuning of an existing method.
The study tackled the problem of detecting contextual anomalies in smart home air quality data by optimizing hyperparameters for a Bi-LSTM model, resulting in improved performance as measured by Precision, Recall, and F1 scores on two datasets.
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.