Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection

02/21/2022
by   Heejeong Choi, et al.
0

As large-scale time-series data can easily be found in real-world applications, multivariate time-series anomaly detection has played an essential role in diverse industries. It enables productivity improvement and maintenance cost reduction by preventing malfunctions and detecting anomalies based on time-series data. However, multivariate time-series anomaly detection is challenging because real-world time-series data exhibit complex temporal dependencies. For this task, it is crucial to learn a rich representation that effectively contains the nonlinear temporal dynamics of normal behavior. In this study, we propose an unsupervised multivariate time-series anomaly detection model named RAE-MEPC which learns informative normal representations based on multi-resolution ensemble and predictive coding. We introduce multi-resolution ensemble encoding to capture the multi-scale dependency from the input time series. The encoder hierarchically aggregates the temporal features extracted from the sub-encoders with different encoding lengths. From these encoded features, the reconstruction decoder reconstructs the input time series based on multi-resolution ensemble decoding where lower-resolution information helps to decode sub-decoders with higher-resolution outputs. Predictive coding is further introduced to encourage the model to learn the temporal dependencies of the time series. Experiments on real-world benchmark datasets show that the proposed model outperforms the benchmark models for multivariate time-series anomaly detection.

READ FULL TEXT
research
12/09/2021

Ymir: A Supervised Ensemble Framework for Multivariate Time Series Anomaly Detection

We proposed a multivariate time series anomaly detection frame-work Ymir...
research
02/08/2022

Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data

Anomaly detection in multi-variate time series (MVTS) data is a huge cha...
research
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...
research
01/13/2022

Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection

Today's cyber-world is vastly multivariate. Metrics collected at extreme...
research
02/21/2021

Anomaly Detection in Audio with Concept Drift using Adaptive Huffman Coding

In this work, we propose a framework to apply Huffman coding for anomaly...
research
07/10/2020

Multi-future Merchant Transaction Prediction

The multivariate time series generated from merchant transaction history...
research
06/05/2023

PV Fleet Modeling via Smooth Periodic Gaussian Copula

We present a method for jointly modeling power generation from a fleet o...

Please sign up or login with your details

Forgot password? Click here to reset