统计与决策2024，Vol.40Issue(12)：29-34,6.DOI:10.13546/j.cnki.tjyjc.2024.12.005
基于频率分解的机器学习模型预测效果比较
Comparison of Prediction Effect of Machine Learning Models Based on Frequency Decomposition
摘要
Abstract
This paper introduces a combined prediction method of wavelet transform and machine learning,extracts the main features of univariate time series by wavelet transform,and uses different machine learning models for prediction analysis.Differ-ent types of machine learning models are constructed to forecast the Shanghai Composite Index,Hang Seng Index,Nasdaq Index and Nikkei 225 Index.The results are shown as follows:Without adding any explained variables,the data features transformed by wavelet can predict the index return better.By comparing linear models,machine learning models and deep learning models,it is found that linear models perform best in capturing wavelet transform features.Effective dimensionality reduction method is an im-portant means to improve the out-of-sample prediction accuracy of nonlinear models and reduce the time of model training.The prediction accuracy of wavelet transform and Bayesian mixed model is better than that of traditional ARMA model.关键词
深度神经网络/随机梯度下降/长短期记忆神经网络/小波变换/随机森林Key words
deep neural network/stochastic gradient descent/long short-term memory neural networks/wavelet transform/random forest分类
信息技术与安全科学引用本文复制引用
陈煜之,李心悦,方毅..基于频率分解的机器学习模型预测效果比较[J].统计与决策,2024,40(12):29-34,6.基金项目
国家自然科学基金资助项目(71871104) （71871104）
