Gradient Boost with Convolution Neural Network for Stock Forecast

被引：4

作者：

Liu, Jialin ^{[1
]}

Lin, Chih-Min Min ^{[2
]}

Chao, Fei ^{[1
]}

机构：

[1] Xiamen Univ, Sch Informat, Dept Artificial Intelligence, Xiamen 361005, Fujian, Peoples R China

[2] Yuan Ze Univ, Dept Elect Engn, Taoyuan 320, Taiwan

来源：

ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS (UKCI 2019) | 2020年 / 1043卷

基金：

中国国家自然科学基金;

关键词：

Ensemble learning; Deep learning; Stock forecast; PREDICTION;

D O I：

10.1007/978-3-030-29933-0_13

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Market economy closely connects aspects to all walks of life. The stock forecast is one of task among studies on the market economy. However, information on markets economy contains a lot of noise and uncertainties, which lead economy forecasting to become a challenging task. Ensemble learning and deep learning are the most methods to solve the stock forecast task. In this paper, we present a model combining the advantages of two methods to forecast the change of stock price. The proposed method combines CNN and GBoost. The experimental results on six market indexes show that the proposed method has better performance against current popular methods.

引用

页码：155 / 165

页数：11

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