神经网络、径向基函数网络和支持向量回归预测金价的比较分析

Khanoksin Suranart, S. Kiattisin, A. Leelasantitham
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引用次数: 3

摘要

本文对神经网络、径向基函数网络和支持向量回归预测黄金价格的比较进行了研究和分析。其中神经网络径向基函数网络和支持向量回归是一种利用黄金短期价格的细节来学习机器的方法。此短期价格的持续时间为2008年6月至2013年4月,收集的详细信息将分为两部分,即月度详细信息和每周详细信息。每月细节的细节将提前3个月预测,每周细节将提前3周预测。这些细节将通过偏差、完全平均值、平均平方误差、平均误差和绝对平均误差值来测量精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of comparisons for Forecasting Gold Price using Neural Network, Radial Basis Function Network and Support Vector Regression
This research is done to study and analyze the comparison for forecasting the gold price using Neural Network, Radial Basis Function Network, and Support Vector Regression. Which the neural network radial basis function network and support vector regression is a method of learning about the machine by using the details of the of the short term prices of the gold. The duration of this short term price is from June in the year 2008 until April 2013, the details collected will be broken up into two parts, which is Monthly details, and weekly detail. The details of the monthly detail will be predicted 3 months ahead and for the weekly details will be predicted 3 weeks ahead. The details will be measured for accuracy by the deviation, the complete average value, average squared error, average error, and the absolute average error value.
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