计算机工程与应用2025，Vol.61Issue(6)：229-243,15.DOI:10.3778/j.issn.1002-8331.2311-0126
IMGAF-RLNet模型的股指趋势预测研究
IMGAF-RLNet Model for Stock Index Trend Forecasting
摘要
Abstract
Aiming at the dynamic instability and long-term dependence of financial time series,an IMGAF-RLNet model based on deep learning algorithm is constructed to predict the rise and fall trends of large and medium cap indices in the Chinese stock market.IMGAF-RLNet uses the Gramian angular field method to encode the different feature sequences of the target stock index and the constituent stocks based on Spearman rank correlation coefficient screening into the Gramian difference angle field matrix.Then,the obtained matrix sequence is constructed as a multi-dimensional tensor input to a CNN classifier residual network(ResNet)screened based on the classification results of the pre-trained model for feature extraction,and a long short term memory network(LSTM)is added to learn the temporal features of the stock index data,Finally,the local features extracted by ResNet and the overall features extracted by LSTM are used to complete the classi-fication and prediction of stock index trends through a fully connected network.The CSI 300,SSE 50,and CSI 500 indi-ces are selected as the research subjects.The experiment shows that the accuracy of short-term,medium,and long-term trend prediction for the three stock indices is above 59%,with the best prediction window and classification accuracy are 40,20,20 and 62.65%,63.68%,61.85%respectively.关键词
股指趋势预测/数据增强/格拉姆角场/残差神经网络/长短时记忆网络Key words
stock index trend prediction/data augmentation/Gramian angular field/residual network/long short term memory network(LSTM)分类
信息技术与安全科学引用本文复制引用
张菊平,李路..IMGAF-RLNet模型的股指趋势预测研究[J].计算机工程与应用,2025,61(6):229-243,15.基金项目
国家自然科学基金(11971302,62173222). （11971302,62173222）
