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Neuron-Adaptive Higher Order Neural-Network Models for Automated Financial Data Modeling
journal contribution
posted on 2023-05-16, 13:13 authored by Zhang, M, Shuxiang XuShuxiang Xu, Fulcher, JReal-world financial data is often nonlinear, comprises high-frequency multipolynomial components, and is discontinuous (piecewise continuous). Not surprisingly, it is hard to model such data. Classical neural networks are unable to automatically determine the optimum model and appropriate order for financial data approximation. We address this problem by developing neuron-adaptive higher order neural-network (NAHONN) models. After introducing one-dimensional (1-D), two-dimensional (2-D), and n-dimensional NAHONN models, we present an appropriate learning algorithm. Network convergence and the universal approximation capability of NAHONNs are also established. NAHONN Group models (NAHONGs) are also introduced. Both NAHONNs and NAHONGs are shown to be "open box" and as such are more acceptable to financial experts than classical (closed box) neural networks. These models are further shown to be capable of automatically finding not only the optimum model, but also the appropriate order for specific financial data.
History
Publication title
IEEE Transactions on Neural NetworksVolume
13Pagination
188 - 204ISSN
1045-9227Department/School
School of Information and Communication TechnologyPublisher
IEEE Computer SocietyPlace of publication
Los Alamitos, CaliforniaRepository Status
- Restricted