兵工自动化2024，Vol.43Issue(12)：35-37,54,4.DOI:10.7690/bgzdh.2024.12.009
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
李慧玲1
作者信息
- 1. 国网河北省电力有限公司信息通信分公司,石家庄 050020
- 折叠
摘要
Abstract
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Key words
financial big data/data mining/time series data analysis/sequential Bayesian/stock simulation分类
信息技术与安全科学引用本文复制引用
李慧玲..基于数据挖掘的金融时序数据分析[J].兵工自动化,2024,43(12):35-37,54,4.
