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In recent years, outlier detection has attained great attention with machine learning techniques due to its wide range of applications. By considering the input data’s distributive nature and large dimensionality, outlier detection becomes a challenging issue. Robust outlier detection systems are crucial for data pattern prediction without labeled data. This research develops a novel approach based on stacking auto encoders over Long-Short Term Memory (LSTM) for outlier prediction. The detection accuracy of outlier detection is improved with the hyperparameters optimized with the Chaotic Gravitational Search Algorithm (CGSA). CGSA minimizes the training loss with enhanced detection accuracy in the proposed outlier detection process. The auto encoder in outlier detection transforms the input into a latent space representation to generate the original input sequence. The involvement of learning parameters computes and minimizes the errors between input and generated sequences. The proposed work is experimented and compared with state-of-the-art approaches of recent research. Using the proposed approach, the performance of outlier prediction is improved with an accuracy of98.6%, sensitivity of 96.1%, specificity of 97.8%, G-mean of 96%, Area Under Curve (AUC) of 0.935, Hit rate of 92.3%. Also, the outlier detection errors are minimized, showing the proposed approach’s efficiency.

CGSA optimized LSTM auto encoder for outlier detection