Year: 2025
Type: article
Abstract: In this study, we investigate Bitcoin price volatility from December 15, 2014 to January 29, 2024 using an integrated, multisource feature set and an optimization-learning pipeline that couples Taguchi orthogonal arrays with a backpropagation network (BPN) implemented in MATLAB.Publicly available market variables were prioritized and nonquantifiable exogenous shocks were not modeled; Taguchi screening identified critical predictors and simultaneously tuned control factors (network specification, hidden-neuron count, and currency inclusion), after which the BPN was trained on aligned weekly (n = 573) and monthly (n = 108) datasets to ensure cross-market comparability.Model accuracy, assessed by mean absolute percentage error (MAPE), improved substantially after Taguchi-guided selection and configuration-weekly MAPE decreased from 3.23% to 0.36% and monthly MAPE from 6.32% to 0.07%demonstrating the efficacy of the proposed optimization framework.Out-of-sample forecasts for February-April 2025 achieved predominantly sub-10% MAPE, while high-error instances were analyzed and attributed to contributing factors, yielding decision-relevant insights for practitioners and researchers.Collectively, the results show that systematic variable selection and orthogonal-array-based model design materially enhance neural forecasts of cryptocurrency prices and provide a reproducible pathway to accurate, time-efficient prediction.
Source: Sensors and Materials
DOI: 10.18494/sam5774
Authors: Chi-Han Chen, Shui-Chuan Chen, Wen-Zhe Hsu
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $748
OpenAlex ID: W4417475835
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