Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder

02/02/2021
by   Longyuan Li, et al.
40

Deep generative models have demonstrated their effectiveness in learning latent representation and modeling complex dependencies of time series. In this paper, we present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of multi-dimensional time series. Our model is based on Variational Auto-Encoder (VAE), and its backbone is fulfilled by a Recurrent Neural Network to capture latent temporal structures of time series for both generative model and inference model. Specifically, our model parameterizes mean and variance for each time-stamp with flexible neural networks, resulting in a non-stationary model that can work without the assumption of constant noise as commonly made by existing Markov models. However, such a flexibility may cause the model fragile to anomalies. To achieve robust density estimation which can also benefit detection tasks, we propose a smoothness-inducing prior over possible estimations. The proposed prior works as a regularizer that places penalty at non-smooth reconstructions. Our model is learned efficiently with a novel stochastic gradient variational Bayes estimator. In particular, we study two decision criteria for anomaly detection: reconstruction probability and reconstruction error. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 11

page 14

10/09/2019

Sequential VAE-LSTM for Anomaly Detection on Time Series

In order to support stable web-based applications and services, anomalie...
05/18/2021

Stacking VAE with Graph Neural Networks for Effective and Interpretable Time Series Anomaly Detection

In real-world maintenance applications, deep generative models have show...
06/20/2021

Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density Estimation

Learning generative probabilistic models is a core problem in machine le...
03/24/2021

Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving Noise

Road accident can be triggered by wet road because it decreases skid res...
09/08/2021

DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation

The Automatic Dependent Surveillance Broadcast protocol is one of the la...
11/15/2021

TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation

Recent work in synthetic data generation in the time-series domain has f...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.