[Paper Review] A Joint Model for Anomaly Detection and Trend Prediction on IT Operation Series

Run-Qing Chen, Guanghui Shi+2 more|arXiv (Cornell University)|Oct 9, 2019

Network Security and Intrusion Detection4 citations

TL;DR

This paper proposes ADTP, a joint model combining variational auto-encoder (VAE) and long short-term memory (LSTM) networks to simultaneously perform anomaly detection and trend prediction in IT operation time series. By using VAE-reconstructed clean sequences as input to LSTM, the model enhances robustness to noise and captures long-term patterns, improving both anomaly detection and prediction performance, as validated on two benchmark datasets.

ABSTRACT

Anomaly detection and trend prediction are two fundamental tasks in automatic IT systems monitoring. In this paper, a joint model Anomaly Detector & Trend Predictor (ADTP) is proposed. In our design, the variational auto-encoder (VAE) and long short-term memory (LSTM) are joined together to address both anomaly detection and trend prediction. The prediction block (LSTM) takes clean input from the reconstructed time series by VAE, which makes it robust to the anomalies and noise. In the meantime, the LSTM block maintains the long-term sequential patterns, which are out of the sight of a VAE encoding window. This leads to the better performance of VAE in anomaly detection than it is trained alone. In the whole processing pipeline, the spectral residual analysis is integrated with VAE and LSTM to boost the performance of both. The superior performance on two tasks is confirmed with the experiments on two challenging evaluation benchmarks.

Motivation & Objective

• Address the dual challenge of anomaly detection and trend prediction in IT operation monitoring systems.
• Overcome the limitations of standalone VAEs in capturing long-term dependencies and handling noisy data.
• Improve prediction accuracy and anomaly detection performance by integrating spectral residual analysis with deep learning components.
• Develop a unified framework that leverages reconstruction quality and sequential modeling for enhanced time series understanding.

Proposed method

• Employ a variational auto-encoder (VAE) to reconstruct the input time series and filter out noise and anomalies.
• Feed the VAE-reconstructed clean sequences into an LSTM network to model long-term temporal dependencies and enable trend prediction.
• Integrate spectral residual analysis into the pipeline to enhance sensitivity to anomalies and improve reconstruction quality.
• Train the VAE and LSTM components jointly to optimize both anomaly detection and prediction objectives.
• Use the reconstructed sequence from VAE as input to LSTM, ensuring the prediction block operates on denoised, stable data.
• Leverage the complementary strengths of VAE (representation learning and denoising) and LSTM (sequential modeling) in a unified architecture.

Experimental results

Research questions

• RQ1 Can a joint model effectively perform both anomaly detection and trend prediction in IT operation time series?
• RQ2 How does using VAE-reconstructed sequences improve LSTM-based trend prediction in the presence of noise and anomalies?
• RQ3 To what extent does integrating spectral residual analysis enhance the performance of the joint model?
• RQ4 Does the joint training of VAE and LSTM lead to better anomaly detection than training VAE alone?

Key findings

• The joint ADTP model achieves superior performance on both anomaly detection and trend prediction tasks compared to baseline models.
• Using VAE-reconstructed sequences as input to LSTM improves prediction robustness and accuracy by reducing the impact of noise and anomalies.
• The integration of spectral residual analysis enhances the sensitivity of the model to anomalies, improving detection performance.
• The VAE in the ADTP framework achieves better anomaly detection performance than when trained in isolation, due to improved reconstruction on cleaner, long-term-aware sequences.
• The model demonstrates strong generalization on two challenging real-world IT operation benchmarks, confirming its effectiveness in practical monitoring scenarios.