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

TL;DR

DTAAD introduces a lightweight Transformer-based model combining dual TCNs for accurate and fast anomaly detection and diagnosis in multivariate time series, outperforming existing methods on multiple datasets.

Contribution

The paper presents DTAAD, a novel lightweight model integrating Transformer and dual TCNs with feedback mechanisms for improved anomaly detection in multivariate time series.

Findings

DTAAD outperforms existing methods in detection and diagnosis accuracy.

DTAAD achieves an 8.38% increase in F1 score over baselines.

DTAAD reduces training time by 99%.

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.