[1]刘颖, 李惠迪, 谭博元. 基于多源数据深度融合的金融时间序列预测[J]. 统计与决策, 2022, 38(23): 52-56.

[2]HOSEINZADE E, HARATIZADEH S. CNNpred: CNN-based stock market prediction using a diverse set of variables[J]. Expert Systems with Applications, 2019, 129: 273-285.

[3]QIU J, WANG B, ZHOU C. Forecasting stock prices with long-short term memory neural network based on attention mechanism[J]. PloS One, 2020, 15(1): e0227222.

[4]孙瑞奇. 基于LSTM神经网络的美股股指价格趋势预测模型的研究[D]. 北京: 首都经济贸易大学, 2016.

[5]PATEL M, JARIWALA K, CHATTOPADHYAY C. A systematic review on graph neural network-based methods for stock market forecasting[J]. ACM Computing Surveys, 2025, 57(2): 1-38.

[6]WANG T, GUO J, SHAN Y, et al. A knowledge graph–GCN–community detection integrated model for large-scale stock price prediction[J]. Applied Soft Computing, 2023, 145: 110595.

[7]耿立校, 刘丽莎, 李恒昱. 多源异构数据融合驱动的股票指数预测研究[J]. 计算机工程与应用, 2021, 57(20): 142-149.

[8]蒋雨芯, 魏先华. 融合多源数据的股指预测研究[J]. 科技促进发展, 2022, 18(3): 322-331.

[9]LIU M, SHIN S. Adaptive multi-modal deep learning for financial market prediction: a multi-scale attention approach[J]. Journal of the Korea Society of Computer and Information, 2025, 30(12): 297-304.

[10]BAO W, CAO Y, YANG Y, et al. Data-driven stock forecasting models based on neural networks: a review[J]. Information Fusion, 2025, 113: 102616.

[11]李辰宇. 基于多源数据融合的股票价格预测模型及应用研究[D]. 济南: 山东财经大学, 2023.

[12]JIN Z, YANG Y, LIU Y. Stock closing price prediction based on sentiment analysis and LSTM[J]. Neural Computing and Applications, 2020, 32: 9713-9729.

[13]李海燕. 基于PCA-GA-BPNN模型对股价预测的研究[D]. 西安: 西安理工大学, 2018.

[14]ZHANG X, ZHANG Y, WANG S, et al. Improving stock market prediction via heterogeneous information fusion[J]. Knowledge-Based Systems, 2018, 143: 236-247.

[15]NTI I K, ADEKOYA A F, WEYORI B A. A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction[J]. Journal of Big Data, 2021, 8(1): 17.

[16]AREVALO J, SOLORIO T, MONTES-Y-GÓMEZ M, et al. Gated multimodal networks[J]. Neural Computing and Applications, 2020, 32: 10209-10228.

[17]KIM J, KOH J, KIM Y, et al. Robust deep multi-modal learning based on gated information fusion network[C]//Computer Vision – ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part IV. Cham: Springer, 2018: 90-106.

[18]WANG X, ZHANG X, LIU J, et al. MA-MSTNet: mixed attention-based multi-scale temporal network for stock trend prediction[J]. The Journal of Supercomputing, 2025, 81: 1207.

[19]SUN W, LIU Z, YUAN C, et al. RCSAN: residual enhanced channel spatial attention network for stock price forecasting[J]. Scientific Reports, 2025, 15(1): 21800.

[20]罗云芳, 张广莹. 基于卷积与注意力增强的股票价格预测方法[J]. 云南民族大学学报(自然科学版), 2025, 34(5): 572-581.

[21]阮俊豪, 王康, 丰天韵, 等. 基于Transformer的股票预测方法[J]. 计算机与数字工程, 2025, 53(5): 1375-1380, 1433.

[22]付炫烨. 基于时空注意力网络的股票趋势预测模型的研究与实现[D]. 北京: 北京邮电大学, 2024.

[23]徐钦豪. 基于CNN-GRU的Transformer股价预测模型[D]. 南昌: 江西财经大学, 2025.

[24]MARADIYA D, JINDAL A, JOS C. Integrating price and textual data for next-day stock movement prediction: a study using StockNet dataset[J]. Discover Data, 2025, 3(1): 34.

[25]黄外流. 融合同行业数据的图神经网络股票价格预测研究[D]. 成都: 电子科技大学, 2024.

[26]刘伟. 基于图卷积混合网络模型的股票趋势预测[D]. 哈尔滨: 哈尔滨理工大学, 2022.

[27]孔壮壮. 基于时空图卷积神经网络的股票相关性预测研究[D]. 沈阳: 辽宁大学, 2023.

[28]杨震. 结合股票多视角数据特征与动态市场相关性信息的股票趋势预测模型[D]. 济南: 山东大学, 2023.

[29]王沛钰. 基于多源数据融合的氢能股票预测与推荐系统的实现[D]. 成都: 四川师范大学, 2025.