基于数据挖掘的金融时序数据分析  

Analysis of Financial Time Series Data Based on Data Mining

作　　者：李慧玲 Li Huiling(Information Communication Branch,State Grid Hebei Electric Power Co.,Ltd.,Shijiazhuang 050020,China)

机构地区：[1]国网河北省电力有限公司信息通信分公司,石家庄050020

出　　处：《兵工自动化》2024年第12期35-37,54,共4页Ordnance Industry Automation

摘　　要：为提高金融时序数据分析评估及预测性能,在研究数据挖掘、极大似然估计、序贯参数学习等模型基础上,设计一种序贯贝叶斯学习方法来估计非对称广义自回归条件异方差(autoregressive moving average,GARCH)模型。考虑杠杆效应,描述收益率和波动率之间的负相关关系,从而解决股票模拟模型估计中的复杂数值问题。通过仿真分析,结果表明:该模型能较好地模拟股票波动及价格趋势,具备有效性。In order to improve the performance of financial time series data analysis,evaluation and prediction,a sequential Bayesian learning method is designed to estimate the asymmetric generalized autoregressive moving average(GARCH)model based on the study of data mining,maximum likelihood estimation and sequential parameter learning.The leverage effect is considered to describe the negative correlation between return and volatility,thus solving the complex numerical problems in the estimation of stock simulation models.Through the simulation analysis,the results show that the model can better simulate the stock volatility and price trends,and is effective.

关 键 词：金融大数据 数据挖掘 时序数据分析 序贯贝叶斯 股票模拟 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]