Anomaly detection is an important research area in a diverse range of real-world applications. Although many algorithms … have been proposed to address anomaly detection for numerical datasets, categorical and mixed datasets remain a significant … categorical anomaly detection approach, offering two key contributions to existing methods. First, a novel surprisal-based anomaly …

Time series anomaly detection plays a critical role in various applications, from finance to industrial monitoring …-Supervised Representation Learning) for robust anomaly detection. TDSRL leverages synthetic anomaly segments which are artificially generated to … in relation to the entire time series, while the other focuses on local anomaly boundaries. Additionally, we introduce a …

: anomaly detection based on multivariate normal distribution and deep reinforcement learning using a Deep Q-Network. While … anomaly detection effectively identifies deviations from normal transaction patterns, its static nature limits adaptability in … the DQN are modest compared to anomaly detection, its capacity for online learning and policy refinement enables long …

disclosure using sentence embeddings, multivariate anomaly detection, and explainable artificial intelligence. Prior research … historical baseline through unsupervised anomaly detection. Using Item 1A Risk Factors from Wells Fargo and JPMorgan Chase …. Isolation-based modeling provides stable and discriminative anomaly scores in this setting, and SHAP decomposition highlights …

Time series anomaly detection plays a critical role in various applications, from finance to industrial monitoring …-Supervised Representation Learning) for robust anomaly detection. TDSRL leverages synthetic anomaly segments which are artificially generated to … in relation to the entire time series, while the other focuses on local anomaly boundaries. Additionally, we introduce a …

This study empirically examines the joint effect of financial anomaly risk and ESG performance on accounting … conservatism using accrual models, market models, and earnings time-series models. Financial anomaly scores are obtained using … unsupervised machine learning to identify reporting anomalies for firms. Our findings suggest that higher financial anomaly risk is …