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This paper proposes MoFE, a novel deep learning framework integrating Fourier Neural Operators within a Mixture-of-Experts architecture to address challenges in cryptocurrency price forecasting, achieving state-of-the-art performance in Bitcoin price prediction.
This paper establishes quantitative Sobolev approximation bounds for neural operators, proving that operators can be uniformly approximated with explicit complexity-error relations. It validates these theoretical bounds using Fourier Neural Operators on the Burgers' equation, demonstrating that Sobolev-space approximation theory accurately predicts scaling behavior.