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Anomaly Detection using Supervised Learning and Multiple Statistical Methods

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.