MambaStock: Selective state space model for stock prediction

R. E. Kalman, “A new approach to linear filtering and prediction problems,” Journal of Fluids Engineering , vol. 82, no. 1, pp. 35–44, 1960

1960

Earlier work this paper cites.

G. Box and G. Jenkins, Time series analysis: forecasting and control . Holden-Day, 1970

1970

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.

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.

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

Earlier work this paper cites.

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

2018

Earlier work this paper cites.

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

2021

Cited alongside, same era.

C. Jin, Z. Shi, H. Zhang, and Y. Yin, “Predicting lncrna–protein interactions based on graph autoencoders and collaborative training,” in IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Houston, USA, 9-12 December , 2021

2021

Cited alongside, same era.

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

Original

2021

Cited alongside, same era.

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

Later among the works it cites.

K. Lin, X. Quan, C. Jin, Z. Shi, and J. Yang, “An interpretable double-scale attention model for enzyme protein class prediction based on transformer encoders and multi-scale convolutions,” Frontiers in Genetics , vol. 13, p. 885627, 2022

2022

Later among the works it cites.

——, “Differential equation and probability inspired graph neural networks for latent variable learning,” arXiv preprint arXiv:2202.13800 , 2022

Original

2022

Later among the works it cites.

Z. Shi, Y. Hu, G. Mo, and J. Wu, “Attention-based cnn-lstm and xgboost hybrid model for stock prediction,” arXiv preprint arXiv:2204.02623 , 2022

Original

2022

Later among the works it cites.