TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks (2008.09567v2)

Abstract: Anomaly detection in time series data is a significant problem faced in many application areas such as manufacturing, medical imaging and cyber-security. Recently, Generative Adversarial Networks (GAN) have gained attention for generation and anomaly detection in image domain. In this paper, we propose a novel GAN-based unsupervised method called TAnoGan for detecting anomalies in time series when a small number of data points are available. We evaluate TAnoGan with 46 real-world time series datasets that cover a variety of domains. Extensive experimental results show that TAnoGan performs better than traditional and neural network models.

• The paper introduces a GAN-based framework with LSTM layers for accurate anomaly detection in time series data.
• TAnoGAN outperforms eight baseline models on NAB datasets by leveraging adversarial training.
• The model maps data into latent space to compute reconstruction losses, effectively addressing the challenge of limited data.

Overview of "TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks"

The academic paper titled "TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks" presents a novel methodology for detecting anomalies in time series data utilizing Generative Adversarial Networks (GANs). This research addresses the challenge of anomaly detection in time series when only limited data points are available, a common issue in contexts such as smart infrastructure, cybersecurity, and medical applications.

Methodology and Architecture

TAnoGAN is an unsupervised anomaly detection framework designed to model the normal behavior of time series data by leveraging the capabilities of GANs. This model consists of two primary components: a generator that learns to produce realistic data points and a discriminator trained to distinguish between real and generated data, optimizing their performance through adversarial processes. Notably, TAnoGAN employs Long Short-Term Memory (LSTM) layers within both the generator and discriminator, accommodating the temporal dependencies inherent in time series data.

Key innovations in TAnoGAN's architecture include the use of progressively increasing hidden units in the generator's LSTM layers, facilitating the learning process with small datasets. The generative model endeavors to map real data points into a latent space and back, estimating anomaly scores based on reconstruction losses. This dual-process mapping is crucial in identifying deviations from learned data distributions indicative of anomalies.

Empirical Evaluation

The effectiveness of TAnoGAN is demonstrated using the Numenta Anomaly Benchmark (NAB) collection, comprising 46 diverse real-world time series datasets drawn from various domains. A rigorous assessment against eight baseline models reveals TAnoGAN’s superior performance across measures such as F1 score, Cohen Kappa, and Precision. This improvement underscores the efficacy of adversarial training over traditional anomaly detection techniques.

Contributions and Implications

This research makes significant contributions by:

1. Introducing a GAN-based method tailored for small datasets typically observed in real-world applications.
2. Demonstrating the architectural benefits of TAnoGAN which leverage adversarial training to enhance anomaly detection capabilities in time series data.
3. Establishing the robustness of TAnoGAN through extensive empirical validation against established traditional and neural network-based models.

The theoretical implications of this paper suggest that GANs, structured appropriately, can effectively model complex data distributions, providing a scalable solution for anomaly detection tasks in constrained data scenarios. Practically, TAnoGAN's approach offers scalability and adaptability in monitoring systems across critical industries, enhancing predictive maintenance capabilities and anomaly resolution processes.

Future Directions

The paper opens potential avenues for further exploration, such as enhancing model stability and adjusting architecture to improve performance with even fewer data points. Additionally, future studies might explore the integration of TAnoGAN with large-scale datasets, expanding its applicability and fine-tuning detection capabilities in expansive data environments.

In summary, TAnoGAN is positioned as a valuable tool for developers and data scientists facing anomaly detection challenges in dynamic and resource-constrained settings, showcasing GANs' promise in handling time series data intricacies.