Robust Anomaly Detection for Time-series Data

02/06/2022
by   Min Hu, et al.
70

Time-series anomaly detection plays a vital role in monitoring complex operation conditions. However, the detection accuracy of existing approaches is heavily influenced by pattern distribution, existence of multiple normal patterns, dynamical features representation, and parameter settings. For the purpose of improving the robustness and guaranteeing the accuracy, this research combined the strengths of negative selection, unthresholded recurrence plots, and an extreme learning machine autoencoder and then proposed robust anomaly detection for time-series data (RADTD), which can automatically learn dynamical features in time series and recognize anomalies with low label dependency and high robustness. Yahoo benchmark datasets and three tunneling engineering simulation experiments were used to evaluate the performance of RADTD. The experiments showed that in benchmark datasets RADTD possessed higher accuracy and robustness than recurrence qualification analysis and extreme learning machine autoencoder, respectively, and that RADTD accurately detected the occurrence of tunneling settlement accidents, indicating its remarkable performance in accuracy and robustness.

READ FULL TEXT

page 11

page 13

page 15

03/27/2020

Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing (Technical Report)

Errors are prevalent in time series data, such as GPS trajectories or se...
02/21/2022

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

As large-scale time-series data can easily be found in real-world applic...
04/14/2022

LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data

Anomaly detection for indoor air quality (IAQ) data has become an import...
08/27/2021

Anomaly Detection on IT Operation Series via Online Matrix Profile

Anomaly detection on time series is a fundamental task in monitoring the...
07/25/2020

Improving Robustness on Seasonality-Heavy Multivariate Time Series Anomaly Detection

Robust Anomaly Detection (AD) on time series data is a key component for...
11/10/2020

Building an Automated and Self-Aware Anomaly Detection System

Organizations rely heavily on time series metrics to measure and model k...
08/03/2022

Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data

Time series anomaly detection (TSAD) is an important data mining task wi...