Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction

A. Graves, “Long short-term memory,” Neural Computation , vol. 9, no. 8, pp. 1735–1780, 1997

1997

Earlier work this paper cites.

G. Zhang, “Time series forecasting using a hybrid arima and neural network model,” Neurocomputing , vol. 50, pp. 159–175, 2003

2003

Earlier work this paper cites.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” Journal of Machine Learning Research , vol. 15, no. 1, pp. 1929–1958, 2014

2014

Earlier work this paper cites.

I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in NeurIPS , 2014

2014

Earlier work this paper cites.

D. P. Kingma and J. Ba, “Adam: a method for stochastic optimization,” in 3rd International Conference for Learning Representations(ICLR) , 2015

2015

Earlier work this paper cites.

T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in KDD , 2016

2016

Earlier work this paper cites.

S. Wiseman and A. M. Rush, “Sequence-to-sequence learning as beam-search optimization,” in EMNLP , 2016

2016

Cited alongside, same era.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in NeurIPS , 2017

2017

Cited alongside, same era.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” CoRR , 2018

2018

Cited alongside, same era.

C. Jin, J. Gao, Z. Shi, and H. Zhang, “Attcry: Attention-based neural network model for protein crystallization prediction,” Neurocomputing , vol. 463, pp. 265–274, 2021

2021

Cited alongside, same era.

C. Jin, Z. Shi, W. Li, and Y. Guo, “Bidirectional lstm-crf attention-based model for chinese word segmentation,” arXiv preprint arXiv:2105.09681 , 2021

Original

Z. Shi, “Incorporating transformer and lstm to kalman filter with em algorithm for state estimation,” arXiv preprint arXiv:2105.00250 , 2021

Original

2021

Later among the works it cites.

Z. Shi, H. Zhang, C. Jin, X. Quan, and Y. Yin, “A representation learning model based on variational inference and graph autoencoder for predicting lncrna-disease associations,” BMC Bioinformatics , vol. 22, p. 136, 2021

2021

Later among the works it cites.

C. Jin, Z. Shi, C. Kang, K. Lin, and H. Zhang, “Tlcrys: Transfer learning based method for protein crystallization prediction,” International Journal of Molecular Sciences , vol. 23, p. 972, 1 2022

2022

Closest in time.

C. Jin, Z. Shi, K. Lin, and H. Zhang, “Predicting mirna-disease association based on neural inductive matrix completion with graph autoencoders and self-attention mechanism,” Biomolecules , vol. 12, no. 1, p. 64, 2022

2022

Closest in time.