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Artificial Sine and Cosine Trigonometric Higher Order Neural Networks for Financial Data Prediction

Artificial Sine and Cosine Trigonometric Higher Order Neural Networks for Financial Data Prediction
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Author(s): Ming Zhang (Christopher Newport University, USA)
Copyright: 2017
Pages: 29
Source title: Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-0788-8.ch028

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Abstract

This chapter develops two new nonlinear artificial higher order neural network models. They are Sine and Sine Higher Order Neural Networks (SIN-HONN) and Cosine and Cosine Higher Order Neural Networks (COS-HONN). Financial data prediction using SIN-HONN and COS-HONN models are tested. Results show that SIN-HONN and COS-HONN models are good models for financial data prediction compare with Polynomial Higher Order Neural Network (PHONN) and Trigonometric Higher Order Neural Network (THONN) models.

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