Makatjane, KatlehoTsoku, Johannes Tshepiso2026-03-192026-03-192026Tsoku, J.T. and Makatjane, K., Deep Learning-Based Pairs Trading: Real-Time Forecasting of Cointegrated Cryptocurrency Pairs. Frontiers in Applied Mathematics and Statistics, 12, p.1749337.HTTPS://DOI: 10.3389/fams.2026.1749337https://hdl.handle.net/10566/22048Statistical arbitrage strategies, including pairs trading, rely on identifying co-movements and static long-term equilibrium relationships between assets, where conventional methods fail to capture non-stationary dynamics, hence reducing trading effectiveness. This study, therefore, addresses this challenge by employing a dynamic co-integration approach combined with deep learning techniques to select suitable cryptocurrency pairs and forecast spread dynamics. The study examines multiple cryptocurrencies, namely: BNB, Ethereum, Litecoin, Ripple, and USDT, using dynamic Johansen co-integration tests to identify pairs with time-varying equilibrium relationships, and model the spread through a Dynamic Weighted Ensemble of Deep Neural Network and Long Short-Term Memory. Forecasting accuracy, trading performance, and predictive uncertainty are evaluated using error metrics, trading outcomes, and 99% prediction intervals. The results indicate that only those cryptocurrencies with dynamically coherent relationships are suitable for mean-reversion strategies. Furthermore, the study found that the Dynamic Weighted Ensemble achieves the best predictive accuracy. At the same time, LSTM captures proportional temporal dynamics effectively, and the ensemble-driven trading signals generate timely buy and sell decisions with low-lag execution and robust management of market volatility. These findings, therefore, highlight the advantages of combining dynamic co-integration and adaptive deep learning for statistical arbitrage.encrytpto priceshybrid forecastingneural networksrisk metricsvolatilityArticle