LSTM-based Anomaly Detection for Non-linear Dynamical System
- URL: http://arxiv.org/abs/2006.03193v1
- Date: Fri, 5 Jun 2020 01:09:36 GMT
- Title: LSTM-based Anomaly Detection for Non-linear Dynamical System
- Authors: Yue Tan, Chunjing Hu, Kuan Zhang, Kan Zheng, Ethan A. Davis and Jae
Sung Park
- Abstract summary: We propose a novel anomaly detection scheme in non-linear dynamical system based on Long Short-Term Memory (LSTM)
We first present the framework of LSTM-based anomaly detection in non-linear dynamical system, including data preprocessing, multi-step prediction and anomaly detection.
Our proposed multi-step prediction method can achieve a higher prediction accuracy than traditional method in wall shear stress dataset.
- Score: 11.797156206007612
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Anomaly detection for non-linear dynamical system plays an important role in
ensuring the system stability. However, it is usually complex and has to be
solved by large-scale simulation which requires extensive computing resources.
In this paper, we propose a novel anomaly detection scheme in non-linear
dynamical system based on Long Short-Term Memory (LSTM) to capture complex
temporal changes of the time sequence and make multi-step predictions.
Specifically, we first present the framework of LSTM-based anomaly detection in
non-linear dynamical system, including data preprocessing, multi-step
prediction and anomaly detection. According to the prediction requirement, two
types of training modes are explored in multi-step prediction, where samples in
a wall shear stress dataset are collected by an adaptive sliding window. On the
basis of the multi-step prediction result, a Local Average with Adaptive
Parameters (LAAP) algorithm is proposed to extract local numerical features of
the time sequence and estimate the upcoming anomaly. The experimental results
show that our proposed multi-step prediction method can achieve a higher
prediction accuracy than traditional method in wall shear stress dataset, and
the LAAP algorithm performs better than the absolute value-based method in
anomaly detection task.
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