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Variational Autoencoder Combined with LSTM for Anomaly Detection and Regime Change Identification in Financial Time Series.

Hakan Khan
Volume 5

Abstract

Financial time series are highly complex, nonlinear, and nonstationary, frequently exhibiting abnormal movements, volatility clustering, structural breaks, and sudden regime transitions caused by market shocks, liquidity stress, macroeconomic announcements, geopolitical events, and investor behavioral changes. Accurate anomaly detection and regime change identification are therefore essential for risk management, algorithmic trading, portfolio protection, and early-warning financial surveillance systems. However, traditional statistical models and conventional machine learning methods often struggle to capture hidden temporal dependencies, latent market structures, and rare abnormal patterns in noisy financial data. This paper proposes a hybrid deep learning framework that combines Variational Autoencoders with Long Short-Term Memory networks for anomaly detection and regime change identification in financial time series. The proposed model leverages the Variational Autoencoder to learn compact probabilistic latent representations of normal market behavior, while the LSTM component captures sequential dependencies and temporal evolution across historical observations. The framework was evaluated using multivariate financial datasets including stock indices, exchange rates, commodities, volatility indices, trading volume, technical indicators, returns, realized volatility, moving averages, momentum indicators, and liquidity-related features. A comprehensive preprocessing pipeline was applied, including missing value imputation, normalization, log-return transformation, rolling-window segmentation, noise reduction, and chronological train-validation-test splitting to avoid look-ahead bias. Anomalies were identified based on reconstruction error, latent-space deviation, and temporal prediction residuals, while regime changes were detected by monitoring shifts in latent distributions and volatility-sensitive sequence patterns over time. Experimental results demonstrate that the proposed VAE-LSTM framework achieves superior anomaly detection performance compared with baseline models including ARIMA, GARCH, Isolation Forest, One-Class SVM, standard Autoencoder, standalone LSTM, and GRU-based approaches. The proposed framework records higher precision, recall, F1-score, AUC-ROC, and detection stability, while reducing false alarms during normal high-volatility periods. Furthermore, latent-space visualization and reconstruction-based explainability were incorporated to distinguish between transient anomalies and persistent regime transitions, enabling clearer interpretation of market stress conditions. The findings indicate that the proposed hybrid VAE-LSTM model provides an adaptive, robust, and interpretable solution for identifying abnormal financial behavior and structural regime shifts, offering practical value for financial institutions, quantitative traders, portfolio managers, and risk-monitoring systems operating in uncertain and rapidly changing market environments.

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References

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