TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks (2009.07769v3)

Abstract: Time series anomalies can offer information relevant to critical situations facing various fields, from finance and aerospace to the IT, security, and medical domains. However, detecting anomalies in time series data is particularly challenging due to the vague definition of anomalies and said data's frequent lack of labels and highly complex temporal correlations. Current state-of-the-art unsupervised machine learning methods for anomaly detection suffer from scalability and portability issues, and may have high false positive rates. In this paper, we propose TadGAN, an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs). To capture the temporal correlations of time series distributions, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. TadGAN is trained with cycle consistency loss to allow for effective time-series data reconstruction. We further propose several novel methods to compute reconstruction errors, as well as different approaches to combine reconstruction errors and Critic outputs to compute anomaly scores. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one. We compare our approach to 8 baseline anomaly detection methods on 11 datasets from multiple reputable sources such as NASA, Yahoo, Numenta, Amazon, and Twitter. The results show that our approach can effectively detect anomalies and outperform baseline methods in most cases (6 out of 11). Notably, our method has the highest averaged F1 score across all the datasets. Our code is open source and is available as a benchmarking tool.

• The paper introduces TadGAN with LSTM-based generators and critics that reconstruct time series to detect anomalies effectively.
• It achieves a superior average F1-score of 0.7 across 11 diverse datasets compared to eight baseline methods.
• The study shows that combining DTW with critic outputs significantly enhances anomaly detection reliability in real-world scenarios.

Evaluating TadGAN for Time Series Anomaly Detection

The paper in question explores the challenging problem of identifying anomalies within time series data using a novel unsupervised approach, TadGAN, which leverages Generative Adversarial Networks (GANs). Anomalies present within time series data are critical for numerous domains, e.g., finance, aerospace, IT, security, and healthcare, but traditional detection methods face limitations in terms of scalability, portability, and false positive rates. The authors introduce TadGAN to address these challenges, utilizing Long Short-Term Memory (LSTM) networks for both the Generator and Critic components of the GAN architecture, trained with a cycle consistency loss to facilitate effective time series reconstruction.

Methodology

The TadGAN framework incorporates two primary components: LSTM-based Generators and Critics that engage in adversarial learning to reconstruct time series data proficiently. The network is trained to minimize the Wasserstein loss between the data distributions in real and generated domains, alongside a cycle consistency loss ensuring reliable mapping between latent representations and the original data. The proposed model computes reconstruction errors in several innovative ways and combines these with outputs from the Critic to produce anomaly scores.

The authors propose three error metrics for detecting anomalies: point-wise differences, area differences, and Dynamic Time Warping (DTW). These facilitate capturing different anomaly types—point anomalies, and collective anomalies, respectively. The anomaly scores are further refined by combining reconstruction errors and Critic outputs either through convex combinations or multiplication, with later analyses suggesting multiplicative combinations yield more reliable anomaly detection results.

Experimental Evaluation

TadGAN is evaluated using 11 diverse datasets from sources like NASA, Yahoo, and Numenta, comprising both synthetic and real-world data. The datasets span a total of 492 signals and 2,349 labeled anomalies. Across these datasets, TadGAN consistently achieves the highest average F1-score (0.7) compared to eight baseline methods, including popular ones like ARIMA, LSTM, and proprietary anomaly detection tools like Microsoft Azure and Amazon DeepAR. This demonstrates the robustness and effectiveness of the TadGAN approach in detecting anomalous time series.

Findings

Compared to baseline models, TadGAN outperforms others in most cases, including complex real-world datasets where context-sensitive anomalies are present. The framework excels particularly well against prediction-based approaches such as LSTM and ARIMA and shows superior performance in most real-world datasets than reconstruction-based models like LSTM Auto-Encoders. However, on point-anomaly heavy synthetic datasets, relatively simpler models like ARIMA sometimes yield competitive scores.

An in-depth breakdown of the model variations within TadGAN shows that using DTW in combination with the Critic output leads to the most favorable anomaly detection performance, underscoring the significance of effective time series similarity measures in enhancing anomaly detection.

Implications and Future Work

This paper contributes significantly to the understanding of anomaly detection within time series by proving the capabilities of GANs in this domain. While the TadGAN framework handles complex temporal correlations effectively due to its structure and adversarial training process, incorporating more nuanced, possibly domain-specific, anomaly detection criteria might further benefit the approach. Future work could explore refining the network’s architecture, optimizing the reconstruction strategies, and integrating domain knowledge to fine-tune anomaly detection capabilities.

Furthermore, the open-source nature of their benchmarking system positions this work as a valuable resource for continued research and development within the field of time series anomaly detection, paving the way for even more sophisticated applications of GANs and other machine learning frameworks.