A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

Abstract

Nowadays, multivariate time series data are increasingly collected in various real world systems, e.g., power plants, wearable devices, etc. Anomaly detection and diagnosis in multivariate time series refer to identifying abnormal status in certain time steps and pinpointing the root causes. Building such a system, however, is challenging since it not only requires to capture the temporal dependency in each time series, but also need encode the inter-correlations between different pairs of time series. In addition, the system should be robust to noise and provide operators with different levels of anomaly scores based upon the severity of different incidents. Despite the fact that a number of unsupervised anomaly detection algorithms have been developed, few of them can jointly address these challenges. In this paper, we propose a Multi-Scale Convolutional Recurrent Encoder-Decoder (MSCRED), to perform anomaly detection and diagnosis in multivariate time series data. Specifically, MSCRED first constructs multi-scale (resolution) signature matrices to characterize multiple levels of the system statuses in different time steps. Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns. Finally, based upon the feature maps which encode the inter-sensor correlations and temporal information, a convolutional decoder is used to reconstruct the input signature matrices and the residual signature matrices are further utilized to detect and diagnose anomalies. Extensive empirical studies based on a synthetic dataset and a real power plant dataset demonstrate that MSCRED can outperform state-of-the-art baseline methods.

References in corpus (9)

• Adam: A Method for Stochastic Optimization
• Neural Machine Translation by Jointly Learning to Align and Translate
• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
• Self-Normalizing Neural Networks
• Toward Supervised Anomaly Detection
• A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
• Deep Structured Energy Based Models for Anomaly Detection
• Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data
• A Better Alternative to Piecewise Linear Time Series Segmentation

Cited by in corpus (6)

• Deep Learning for Anomaly Detection: A Survey
• Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives
• RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks
• Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders
• Visual Analytics of Anomalous User Behaviors: A Survey
• A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data