RAVEN proposes a regime-aware MoE architecture with cumulative importance thresholding and correlation-aware weighting to adaptively select temporal context for non-stationary financial forecasting.
FinMultiTime: A four-modal bilingual dataset for financial time-series analysis.arXiv preprint arXiv:2506.05019
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A semi-synthetic dataset of 7 million context-augmented time series windows with verifier-filtered contexts enables transfer to real-world context-aided forecasting and suggests data quality—not architecture—was the bottleneck.
citing papers explorer
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RAVEN: A Regime-Aware Variable-context Expert Network for Financial Time Series Forecasting
RAVEN proposes a regime-aware MoE architecture with cumulative importance thresholding and correlation-aware weighting to adaptively select temporal context for non-stationary financial forecasting.
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Overcoming the Modality Gap in Context-Aided Forecasting
A semi-synthetic dataset of 7 million context-augmented time series windows with verifier-filtered contexts enables transfer to real-world context-aided forecasting and suggests data quality—not architecture—was the bottleneck.