Numerical Signal Design for Crypto Intelligence
DOI:
https://doi.org/10.71204/b2c2rb55Keywords:
Deterministic Numerical Methods, Computational Finance, Cryptocurrency Analysis, Feature Engineering, Scientific ComputingAbstract
This study presents a novel framework that bridges deterministic numerical algorithms with computational finance to support interpretable machine learning applications in cryptocurrency analysis. By applying Newton’s method, the Trapezoidal Rule, and a hybrid Euler–Adams-Bashforth solver, we generate structured numerical features that capture convergence, integration precision, and differential dynamics, respectively. These feature vectors are constructed from both synthetically generated sequences and real AVAX-USD log return data, enabling a direct comparison between theoretical numerical behavior and market-driven fluctuations. A deterministic labeling rule, based on the parity of the integer sum of the features, defines class boundaries with geometric regularity, allowing the K-Nearest Neighbors classifier to operate in a feature space shaped by mathematically grounded transformations. The results reveal that classical numerical methods, when applied to financial time series, produce stable, class-separable patterns that are well-suited for local classification and boundary interpretation. Through decision boundary visualization, error convergence analysis, and joint feature interaction plots, the paper demonstrates that numerical approximation techniques can illuminate latent structural signals in crypto price behavior. This framework advances the integration of scientific computing and data-driven finance, offering a new paradigm for understanding digital asset dynamics through the lens of deterministic modeling.
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Copyright (c) 2025 Sai Zhang, Chongbin Luo, Annike Cai, Yeran Lu (Author)
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