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The paper proposes a Regime-Aware Multi-Modal Learning (RAML) method for Bitcoin price direction prediction that adaptively fuses social sentiment and technical features based on market volatility. Evaluated on hourly data from July 2024 to September 2025, RAML achieves moderate improvements over static fusion baselines.
The paper proposes RAVEN, a Mixture-of-Experts framework that adaptively determines temporal context windows for each input sample to handle non-stationary financial time series. It achieves state-of-the-art performance on financial and traffic benchmarks.