LSCR: Latent Space Coordination Relation for Anomaly Prediction

Abstract

Anomaly time series prediction is a crucial yet challenging task in real-world systems. Existing techniques often require a substantial amount of data to achieve satisfactory performance, posing a significant challenge as anomaly data is scarce and difficult to obtain. Despite attempts to address this issue using traditional machine learning techniques, their effectiveness remains limited, resulting in performance degradation or costly trade-offs. Therefore, in this paper, we propose a novel approach called Latent Space Coordination Relation for Anomaly Prediction to overcome these challenges. Our framework leverages the power of the Variational Autoencoder (VAE) and learns the coordination relations of points in the latent space to detect anomalies. By exploiting the latent space, our method enables effective learning and prediction of anomalies. Additionally, the decoder of the VAE aids in restoring the data, further improving the accuracy of anomaly detection. Experimental results demonstrate that our approach outperforms baseline models when training data is limited. The predicted anomalous signals exhibit lower error rates, highlighting the efficacy of our method. This improvement is attributed to the utilization of the latent space for learning and assisting in anomaly prediction, along with the signal restoration capabilities of the decoder.