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Neuro-symbolic framework enhances financial forecasting with adaptive FCMs and LSTMs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel neuro-symbolic framework for financial forecasting that combines adaptive Fuzzy Cognitive Maps (FCMs) with Long Short-Term Memory (LSTM) networks. This approach addresses the limitations of existing models by capturing both temporal patterns and causal drivers, adapting to market shifts post-training. The framework demonstrated improved performance on NASDAQ Composite data, outperforming standard LSTM models in return-sign accuracy and standalone FCMs in state-directional accuracy. AI

IMPACT This neuro-symbolic approach could lead to more robust and adaptive AI-driven financial analysis tools.

RANK_REASON The cluster contains an academic paper detailing a new methodology for financial forecasting. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.AI →

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

Neuro-symbolic framework enhances financial forecasting with adaptive FCMs and LSTMs

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The cluster contains an academic paper detailing a new methodology for financial forecasting. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Brandon Rodriguez, Abel Deulofeu, Olaoluwa Adigun ·

    Online-Adaptive Fuzzy Cognitive Maps for Neuro-symbolic Financial Forecasting

    arXiv:2611.13388v1 Announce Type: new Abstract: We present a neuro-symbolic framework that pairs online-adaptive Fuzzy Cognitive Maps (FCMs) with long short-term memory (LSTM) networks for financial forecasting. Financial markets exhibit regime shifts and nonlinear dynamics. Exis…

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