An anomaly detection approach based on the combination of LSTM autoencoder and isolation forest for multivariate time series data

It is true that anomaly detection is an important issue that has had a long history in the research community due to its various applications. Literature has recorded various Artificial Intelligence (AI) techniques that have been applied to detect anomalies without having a priori knowledge about them. Anomaly detection approaches for multivariate time series data have still too many unrealistic assumptions to apply to the industry. Our paper, therefore, proposed a new efficiency approach of anomaly detection for multivariate time series data. We specifically developed a new hybrid approach based on LSTM Autoencoder and Isolation Forest (iForest). This approach enables the advantages in extracting good features of the LSTM Autoencoder and the good performance in anomaly detection problems of the iForest. The results show that our approach leads to the improvement of performance significantly in comparison with the One-Class Support Vector Machine (OCSVM) method. Our approach is implemented on simulated data in the fashion industry (FI).