DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series Data

Abstract

Anomaly detection techniques enable effective anomaly detection and diagnosis in multi-variate time series data, which are of major significance for today's industrial applications. However, establishing an anomaly detection system that can be rapidly and accurately located is a challenging problem due to the lack of anomaly labels, the high dimensional complexity of the data, memory bottlenecks in actual hardware, and the need for fast reasoning. In this paper, we propose an anomaly detection and diagnosis model, DTAAD, based on Transformer and Dual Temporal Convolutional Network (TCN). Our overall model is an integrated design in which an autoregressive model (AR) combines with an autoencoder (AE) structure. Scaling methods and feedback mechanisms are introduced to improve prediction accuracy and expand correlation differences. Constructed by us, the Dual TCN-Attention Network (DTA) uses only a single layer of Transformer encoder in our baseline experiment, belonging to an ultra-lightweight model. Our extensive experiments on seven public datasets validate that DTAAD exceeds the majority of currently advanced baseline methods in both detection and diagnostic performance. Specifically, DTAAD improved F1 scores by $8.38\%$ and reduced training time by $99\%$ compared to the baseline. The code and training scripts are publicly available on GitHub at https://github.com/Yu-Lingrui/DTAAD.

References in corpus (12)

• Adam: A Method for Stochastic Optimization
• Neural Machine Translation by Jointly Learning to Align and Translate
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• WaveNet: A Generative Model for Raw Audio
• Deep Learning for Anomaly Detection: A Review
• Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
• Deep Learning for Anomaly Detection: A Survey
• Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
• Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks
• Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case
• Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
• Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection

Cited by in corpus (1)

• Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions