用于金融时间序列预测的神经网络集成模型  

Neural Network Ensemble Models for Financial Time Series Forecasting

作　　者：张晗[1] 王维国[2] ZHANG Han;WANG Weiguo(School of Data Science and Artificial Intelligence,Dongbei University of Finance and Economics,Dalian 116025,China;School of Economics,Dongbei University of Finance and Economics,Dalian 116025,China)

机构地区：[1]东北财经大学数据科学与人工智能学院,大连116025 [2]东北财经大学经济学院,大连116025

出　　处：《北京邮电大学学报》2025年第1期127-132,共6页Journal of Beijing University of Posts and Telecommunications

基　　金：辽宁省科学技术计划应用基础研究计划项目(2023JH2/101600040);辽宁省教育厅基本科研项目(LJKMZ20221598)。

摘　　要：准确预测金融时间序列数据对金融市场的运行和管理起着重要作用。基于神经网络和集成学习思想,将卷积神经网络(CNN)、长短期记忆(LSTM)网络以及自回归移动平均(ARMA)模型在集成框架中进行组合,提出一种新的用于预测金融时间序列数据的ARMA-CNN-LSTM模型。该模型通过CNN-LSTM模型对数据中时空特征进行建模,同时利用ARMA模型对数据的自相关特征进行建模,实现对金融时间序列数据中线性和非线性特征的混合建模。实验结果表明,与基准个体模型相比,所提模型在预测金融时间序列数据的精度和鲁棒性方面都显示出优异的性能。The accurate forecasting of financial time series data holds a significant position in the operation and management of financial markets.Grounded in the notions of neural networks and ensemble learning,the convolutional neural network(CNN),long short-term memory(LSTM)network,and autoregressive moving average(ARMA)model are integrated within an ensemble framework to put forward a novel ARMA-CNN-LSTM model for forecasting financial time series data.The spatiotemporal characteristics within the data are modeled by the CNN-LSTM model,while the autocorrelation features of the data are simultaneously modeled by the ARMA model,achieving hybrid modeling of linear and nonlinear traits in financial time series data.The experimental results show that,compared with the baseline individual models,the proposed model demonstrates excellent performance in both the accuracy and robustness on predicting financial time series data.

关 键 词：金融时间序列 卷积神经网络 长短期记忆 自回归移动平均 集成预测模型 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]