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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17498297 |
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Table of Contents:
- <p><span>As financial markets become increasingly complex and nonlinear, traditional forecasting methods are limited in their performance when faced with high-dimensional, multimodal data and significant volatility. The introduction of artificial intelligence (AI) technology has provided more intelligent and adaptive solutions for financial forecasting. This paper proposes an Adaptive Market Insight Network (AMIN) based on heuristic optimization. This algorithm efficiently models the complex, nonlinear characteristics of financial markets through a multimodal information interaction gating mechanism, dynamic feature weight fusion, volatility-sensitive gradient adjustment, and a semi-random parameter perturbation mechanism. The core of the algorithm lies in the introduction of a novel mathematical model and weight update mechanism to adapt to market dynamics, improving forecast accuracy and model adaptability. This paper details the algorithm's design principles, mathematical formulas, and operational mechanisms, providing a theoretical reference for the application of AI in financial forecasting.</span></p>