Robust Anomaly Detection in Financial Markets Using LSTM Autoencoders and Generative Adversarial Networks

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The hybrid model significantly enhances recall and F4-score in detecting anomalies in financial time series.
Experiments showed robust performance across six stock categories and different macroeconomic regimes.
A novel anomaly injection mechanism simulates market irregularities for effective model validation.
The approach addresses the limitations of data constraints, promoting real-world applicability in financial surveillance.

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Hybrid model improves anomaly detection performance in financial markets, suggesting practical applications.

Yang et al. (2025) studied this question.

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