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New MoFE framework uses Mixture-of-Experts and FNOs for crypto forecasting

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced MoFE, a novel deep learning framework designed for cryptocurrency forecasting. This framework integrates Fourier Neural Operators (FNOs) within a Mixture-of-Experts (MoE) architecture, aiming to capture the complex dynamics of non-stationary cryptocurrency markets. MoFE utilizes adaptive FNO and Convolution dual-domain experts to learn spectral trends and microstructures, with a dynamic gating mechanism for adaptive strategy switching across market regimes. Experiments on Bitcoin data from January 2020 to December 2025 show MoFE achieving state-of-the-art performance in both T+1 and T+5 forecasting horizons, significantly improving directional accuracy and information coefficient, and leading to robust risk-adjusted performance in simulated trading. AI

IMPACT This novel framework could improve the accuracy and profitability of cryptocurrency trading by better capturing market dynamics.

RANK_REASON The cluster contains a research paper detailing a novel deep learning framework for cryptocurrency forecasting. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New MoFE framework uses Mixture-of-Experts and FNOs for crypto forecasting

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The cluster contains a research paper detailing a novel deep learning framework for cryptocurrency forecasting. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Bowen Liu, Mingming Sun ·

    MoFE: A Novel Mixture-of-Experts Framework with Fourier Neural Operators for Cryptocurrency Forecasting

    arXiv:2608.17342v1 Announce Type: cross Abstract: Forecasting cryptocurrency prices remains a formidable challenge due to inherent non-stationarity, abrupt regime shifts, and multi-scale stochastic dependencies. Conventional deep learning models often struggle to capture complex …

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