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RAG4CTS achieves state-of-the-art time-series forecasting by ditching static embeddings for a hierarchical, physics-informed retrieval approach that leverages raw historical regimes.
Aura unlocks more accurate aviation time series forecasting by explicitly modeling how different types of external factors interact with temporal dynamics.
