In the labyrinthine landscape of macroeconomic data, where subtle anomalies can herald significant economic shifts, this paper proposes a novel hybrid framework for their accurate and timely detection. The framework effectively combines an Autoregressive Integrated Moving Average (ARIMA) model for solid baseline forecasting with Long Short-Term Memory (LSTM) networks and a variational autoencoder that incorporates LSTM layers (VAE-LSTM) to capture complex residual patterns analysis. A unique dynamic weighting method, which includes temporal smoothing and differences in macroeconomic states, adaptively fuses the outputs of these models, leveraging their strengths across diverse economic scenarios. The proposed hybrid framework’s efficacy was evaluated on a dataset of 348 macroeconomic indicators from Brazil, Russia, India, China, and South Africa (BRICS) nations, covering 1970 to 2020. Empirical results show the framework outperforms other state-of-the-art (SoTA) methods: ARIMA, LSTM, VAE-LSTM, Autoencoder (AE), Isolation Forest (IF) and One-Class Support Vector Machine (OCSVM) achieving an F1-score of 0.915 with AUC of 0.926 and PR-AUC of 0.839. Furthermore, sensitivity analysis substantiates the framework’s robustness across different weighting configurations, maintaining consistent F1-scores between 0.887 and 0.915. The proposed framework offers a robust and adaptive approach to anomaly detection in complex macroeconomic time series, with potential applications in risk management, policy formulation, and economic forecasting.
Vivekananda et al. (Mon,) studied this question.