LLM-AP: LLM-Based Anomaly Detection with Synthetic Time-Series Data Augmentation

Compression-Based Anomaly Detection for Business Intelligence: A Study of LZ78 and Neural Models in Time Series

Anomaly detection in time series is an important problem in domains where abnormal behavior may indicate critical system failures and labeled data is rarely available, making unsupervised approaches particularly relevant. This paper relies on two compression-based methods for time series anomaly detection. The first approach implements universal probability assignment derived from the Lempel-Ziv 78 compression algorithm, while the second method employs neural lossless compression using an autoregressive recurrent model. Both techniques interpret anomalies as sequences that are difficult to compress under models learned from normal behavior. The methods are evaluated on the Electricity Transformer dataset, focusing on the temporal behavior and statistical properties of the anomaly scores, in order to highlight the complementary characteristics of universal and learned compression strategies. By eliminating the need for costly manual labeling, these unsupervised models provide a scalable and cost-efficient solution for real-time monitoring in resource-constrained industrial environments. This research demonstrates how information-theoretic metrics can be effectively integrated into Business Intelligence systems to facilitate informed decisions regarding predictive maintenance and risk mitigation.

LLM as Detector: An In-context Learning Approach for Tabular Anomaly Detection

LLM-Detector is proposed, a framework that utilizes the in-context learning capacity of LLMs for structured, prompt-conditioned scoring synthesis, enabling LLMs to derive anomaly detection logic from structured normal-state knowledge.

IA-AD: a contrastive framework with Inception-Attention for time series anomaly detection

Time Series-Based Anomaly Detection in Gas Turbines Using TimeWGAN-GP

Reliable early detection of degradation in gas turbines is essential for preventing failures and optimizing maintenance strategies. However, traditional fault-detection methods struggle to capture the complex temporal dynamics that precede abnormal conditions. This study investigates the use of Generative Adversarial Networks (GANs) for time-series modeling and anomaly datasets augmentation, aiming to learn the evolving behavior of turbine systems directly from sensor data and to address the insufficient amount of failure data for anomaly detection. In the first stage, GANs are trained on historical time-series recordings of turbines operating under normal conditions to address data scarcity and enhance model robustness. Validation results show that the generated sequences maintain strong statistical consistency with the original data, accurately reproducing characteristic temporal patterns of healthy operation. The second stage focuses on anomaly detection by integrating real-time sensor inputs with the learned temporal representations from both real and synthetic datasets. The discriminator detects deviations from expected behavior, enabling early identification of abnormal trends and providing insight into degradation trajectories. Experimental findings demonstrate that the proposed framework captures dynamic transitions more effectively than threshold-based methods, predicts the progression of faults, and supports the construction of an expanded anomaly catalog by generating synthetic profiles that resemble documented failure modes. Overall, the approach improves early-warning capabilities and strengthens data-driven maintenance planning.

Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection

This work investigates the zero-shot application of a univariate forecasting FM, TimesFM, to industrial MTSAD on the Secure Water Treatment (SWaT) benchmark, and concludes that the proposed naive zero-shot FMs are unsuitable for MTSAD but promising for change-point detection.

On the Application of Time-Series Foundation Models for Detecting Long-Context Anomalies in Industrial Control Systems

This work explores the application of pre-trained time-series foundation models (FMs) for detecting anomalies in industrial processes and introduces a new time-series forecasting method that filters out suspicious data and uses previously predicted data as input, called Forecast Fallback (FF).