mlsanityData to 14 Oct 2026

ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN

arXiv 2308.06663 ↗First posted 13 Aug 2023cs.LG cs.AIcited by 4

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Abstract (arXiv)

Anomaly detection in time series data, to identify points that deviate from normal behaviour, is a common problem in various domains such as manufacturing, medical imaging, and cybersecurity. Recently, Generative Adversarial Networks (GANs) are shown to be effective in detecting anomalies in time series data. The neural network architecture of GANs (i.e. Generator and Discriminator) can significantly improve anomaly detection accuracy. In this paper, we propose a new GAN model, named Adjusted-LSTM GAN (ALGAN), which adjusts the output of an LSTM network for improved anomaly detection in both univariate and multivariate time series data in an unsupervised setting. We evaluate the performance of ALGAN on 46 real-world univariate time series datasets and a large multivariate dataset that spans multiple domains. Our experiments demonstrate that ALGAN outperforms traditional, neural network-based, and other GAN-based methods for anomaly detection in time series data.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 21 of its 64 references that are in the map. First posted on arXiv 13 Aug 2023.

Builds on

21 of 64 references.

References · 21

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9 more
  1. 2016C-RNN-GAN: Continuous recurrent neural networks with adversarial trainingcited by 132
  2. 2018Efficient GAN-Based Anomaly Detectioncited by 122
  3. 2015Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmarkcited by 88
  4. 2020TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networkscited by 55
  5. 2019A Survey on GANs for Anomaly Detectioncited by 32
  6. 2020TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networkscited by 29
  7. 2018Anomaly Detection using Autoencoders in High Performance Computing Systemscited by 25
  8. 2017Using LSTM Encoder-Decoder Algorithm for Detecting Anomalous ADS-B Messagescited by 6
  9. 2020Misogynistic Tweet Detection: Modelling CNN with Small Datasetscited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Agentic AI for autonomous anomaly management in complex systemscited by 4
  2. 2025Cluster Aggregated GAN (CAG): A Cluster-Based Hybrid Model for Appliance Pattern Generationcited by 1
  3. 2026RareSense: Rarity-Aware Similarity Search for Anomaly Retrieval in Transactional Datacited by 0
  4. 2026StatD2GAN: When Calibration Masks Generator Quality in Held-Out Evaluation of Synthetic Weather Sequencescited by 0