dLSTM: a new approach for anomaly detection using deep learning with delayed prediction

Abstract

In this paper, we propose delayed Long Short-Term Memory (dLSTM), an anomaly detection method for time-series data. We first build a predictive model from normal (non-anomalous) training data, then perform anomaly detection based on the prediction error for observed data. However, there are multiple states in the waveforms of normal data, which may lower prediction accuracy. To deal with this problem, we utilize multiple prediction models based on LSTM for anomaly detection. In this scheme, the prediction accuracy strongly depends on the method of selecting a proper predictive model from multiple possible models. We propose a novel method to determine the proper predictive model for anomaly detection. Our approach provides multiple predicted value candidates in advance and selects the one that is closest to the measured value. We delay the model selection until the corresponding measured values are acquired. Using this concept for anomaly detection, dLSTM selects the proper predictive model to enhance prediction accuracy. In our experimental evaluation using real and artificial data, dLSTM detects anomalies more accurately than methods in comparison.