# A Joint Model for IT Operation Series Prediction and Anomaly Detection

arXiv: https://arxiv.org/abs/1910.03818 · HTML: https://mlsanity.com/paper/1910.03818/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series](https://mlsanity.com/paper/2109.11428/) (2021)
- [Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders](https://mlsanity.com/paper/2105.02965/) (2021)
- [Anomaly Detection in Aeronautics Data with Quantum-compatible Discrete Deep Generative Model](https://mlsanity.com/paper/2303.12302/) (2023)

## Abstract (arXiv)

Status prediction and anomaly detection are two fundamental tasks in automatic IT systems monitoring. In this paper, a joint model Predictor & Anomaly Detector (PAD) is proposed to address these two issues under one framework. In our design, the variational auto-encoder (VAE) and long short-term memory (LSTM) are joined together. The prediction block (LSTM) takes clean input from the reconstructed time series by VAE, which makes it robust to the anomalies and noise for prediction task. 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.