Scopus; WoS

LAGRU: A GRU-Based Model with Local Context Attention for Stock Price Prediction

Năm XB 2026 Tạp chí / Hội thảo Communications in Computer and Information Science DOI / Link https://doi.org/10.1007/978-3-032-21628-1_40 ↗

Tác giả

Tóm tắt

Accurate stock price prediction remains a challenging task due to the volatile and nonlinear nature of financial markets. Nowadays, Long-Short-Term Memory (LSTM) is widely used in stock price prediction and has demonstrated strong performance in capturing temporal...

Tài liệu tham khảo

[1] Chen, Q., Zhang, W., Lou, Y.: Forecasting stock prices using a hybrid deep learning model integrating attention mechanism, multi-layer perceptron, and bidirectional long-short term memory neural network. IEEE Access 8, 117365–117376 (2020)

[2] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling (2014). https://arxiv.org/abs/1412.3555

[3] Gupta, P., Malik, S., Apoorb, K., Sameer, S.M., Vardhan, V., Ragam, P.: Stock market analysis using long short-term model. EAI Endorsed Trans. Scalable Inf. Syst. (2023)

[4] He, B., Gong, E., Li, L., Yang, Y.: A stock price prediction method based on lstm and k-means. Front. Sci. Eng. 3(6) (2023)

[5] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

[6] Hua, Y., Zhao, Z., Li, R., Chen, X., Liu, Z., Zhang, H.: Deep learning with long short-term memory for time series prediction. IEEE Commun. Mag. 57(6), 114–119 (2019)

[7] Mai-Tan, H., Pham-Nguyen, H.N., Long, N.X., Minh, Q.T.: Mining urban traffic condition from crowd-sourced data. SN Comput. Sci. 1(4), 225 (2020)

[8] Mehtab, S., Sen, J.: Stock price prediction using CNN and LSTM-based deep learning models. In: 2021 International Conference on Decision Aid Sciences and Application (DASA), pp. 447–453 (2020)

[9] Minh, Q.T., Pham-Nguyen, H.N., Tan, H.M., Long, N.X.: Traffic congestion estimation based on crowd-sourced data. In: 2019 International Conference on Advanced Computing and Applications (ACOMP), pp. 119–126. IEEE (2019)

[10] Nguyen, T.H., Mai, T.H., Vu, D.Q.: A lightweight transformer-based model for fight recognition. In: Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications, pp. 339–346 (2024)

[11] Pardeshi, K., Gill, S.S., Abdelmoneim, A.: Stock market price prediction: a hybrid LSTM and sequential self-attention based approach (2024)

[12] Phung, T., Vu, D.Q., Mai-Tan, H., Nhung, L.T.: Deep models for mispronounce prediction for Vietnamese learners of English. In: Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications, pp. 682–689 (2022)

[13] Phung, T., Vu, V.D., Mai, T.H.: A pronunciation practice system based on pre-trained deep learning models. In: Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications, pp. 325–332 (2024)

[14] Schmidt, R.M.: Recurrent neural networks (rnns): a gentle introduction and overview (2019). https://arxiv.org/abs/1912.05911

[15] Shah, J., Jain, R., Jolly, V., Godbole, A.: Stock market prediction using bi-directional LSTM. In: IEEE International Conference on Communication Information and Computing Technology (ICCICT), Mumbai, India, 25–27 June 2021 (2021)

[16] Tan, H.M., Pham-Nguyen, H.N., Minh, Q.T., Huu, P.N.: Traffic condition estimation based on historical data analysis. In: 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE), pp. 256–261. IEEE (2021)

[17] Tan, H.M., Quoc, N.K., et al.: Multi-mask learning and vector training for monaural speech separation. Vietnam J. Comput. Sci. 1, 17 (2025)

[18] Tan, H.M., Wang, J.C.: Single channel speech separation using enhanced learning on embedding features. In: 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE), pp. 430–431. IEEE (2021)

[19] Tan, H.M., et al.: Speech separation using augmented-discrimination learning on squash-norm embedding vector and node encoder. IEEE Access 10 (2022)

[20] Tan, H., Minh, L., Minh, T., Quyen, T., Cao-Van, K.: Comparing LSTM models for stock market prediction: a case study with apple’s historical prices. In: Nature of Computation and Communication. ICTCC 2023 (2024)

[21] Vijh, M., Chandola, D., Tikkiwal, V.A., Kumar, A.: Stock closing price prediction using machine learning techniques. Procedia Comput. Sci. 167, 599–606 (2020)

[22] Zhang, J., Ye, L., Lai, Y.: Stock price prediction using CNN-BILSTM-attention model. Mathematics 11(9), 1985 (2023)

[23] Zhao, J., Yang, W., Zhu, F.: A CNN-LSTM-attention model for near-crash event identification on mountainous roads. Appl. Sci. 14(11), 4934 (2024)

[24] Zhou, J.: Predicting stock price by using attention-based hybrid LSTM model. Asian J. Basic Sci. Res. 06(02), 145–158 (2024)