[1] |
YU P F, YAN X S. Stock price prediction based on deep neural networks[J]. Neural Computing and Applications, 2020,32(6):1609-1628.
|
[2] |
NELSON D M Q, PEREIRA A C M, OLIVEIRA R A D. Stock market's price movement prediction with LSTM neural networks[C]// 2017 International Joint Conference on Neural Networks (IJCNN). 2017:1419-1426.
|
[3] |
LI H, SHEN Y Y, ZHU Y M. Stock price prediction using attention-based multi-input LSTM[C]// Proceedings of the 10th Asian Conference on Machine Learning. 2018:454-469.
|
[4] |
FENG F L, CHEN H M, HE X N, et al. Enhancing stock movement prediction with adversarial training[C]// 28th International Joint Conference on Artificial Intelligence. 2019:5843-5849.
|
[5] |
HOSEINZADE E, HARATIZADEH S. CNNpred: CNN-based stock market prediction using a diverse set of variables[J]. Expert Systems with Applications, 2019,129(S):273-285.
|
[6] |
DING Q G, WU S F, SUN H, et al. Hierarchical multi-scale Gaussian transformer for stock movement prediction[C]// Proceedings of the 29th International Joint Conference on Artificial Intelligence. 2021:4640-4646.
|
[7] |
WANG J Y, ZHANG Y,TANG K, et al. Alphastock: A buying-winners-and-selling-losers investment strategy using interpretable deep reinforcement attention networks[C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019:1900-1908.
|
[8] |
DING D Z, ZHANG M, PAN X D, et al. Modeling extreme events in time series prediction[C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019:1114-1122.
|
[9] |
XU Y M, COHEN S B. Stock movement prediction from tweets and historical prices[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 2018:1970-1979.
|
[10] |
FENG F L, HE X N, WANG X, et al. Temporal relational ranking for stock prediction[J]. ACM Transactions on Information Systems (TOIS), 2019,37(2):1-30.
|
[11] |
KIM R, SO C H, JEONG M, et al. Hats: A hierarchical graph attention network for stock movement prediction[J]. arXiv preprint arXiv:1908.07999, 2019.
|
[12] |
HSU Y L, TSAI Y C, LI C T. FinGAT: Financial graph attention networks for recommending top-k profitable stocks[J]. arXiv preprint arXiv:2106.10159, 2021.
|
[13] |
SAWHNEY R, AGARWAL S, WADHWA A, et al. Spatiotemporal hypergraph convolution network for stock movement forecasting[C]// 2020 IEEE International Conference on Data Mining (ICDM). 2020:482-491.
|
[14] |
CUI C, LI X, DU J, et al. Temporal-relational hypergraph tri-attention networks for stock trend prediction[J]. arXiv preprint arXiv:2107.14033, 2021.
|
[15] |
CHENG R, LI Q. Modeling the momentum spillover effect for stock prediction via attribute-driven graph attention networks[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2021,35(1):55-62.
|
[16] |
LI C, SONG D J, TAO D C. Multi-task recurrent neural networks and higher-order Markov random fields for stock price movement prediction: Multi-task RNN and higer-order MRFs for stock price classification[C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019:1141-1151.
|
[17] |
LI W, BAO R H, HARIMOTO K, et al. Modeling the stock relation with graph network for overnight stock movement prediction[C]// Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence. 2021:4541-4547.
|
[18] |
CHEN Y M, WEI Z Y, HUANG X J. Incorporating corporation relationship via graph convolutional neural networks for stock price prediction[C]// Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018:1655-1658.
|
[19] |
WANG H Y, LI S, WANG T J, et al. Hierarchical adaptive temporal-relational modeling for stock trend prediction[C]// Proceedings of the 30th International Joint Conference on Artificial Intelligence. 2021:3691-3698.
|
[20] |
YOO J, SOUN Y, PARK Y, et al. Accurate multivariate stock movement prediction via data-axis transformer with multi-level contexts[C]// Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021:2037-2045.
|
[21] |
LECUN Y, BENGIO Y. Convolutional networks for images, speech, and time series[M]// The Handbook of Brain Theory and Neural Networks. 1998:255-258.
|
[22] |
HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997,9(8):1735-1780.
|
[23] |
ASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017:6000-6010.
|
[24] |
GOODFELLOW I J, SHLENS J, SZEGEDY C. Explaining and harnessing adversarial examples[J]. arXiv preprint arXiv:1412.6572, 2014.
|
[25] |
ZHANG L H, AGGARWAL C C, QI G J. Stock price prediction via discovering multi-frequency trading patterns[C]// Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017:2141-2149.
|
[26] |
ZHANG Z H, ZOHREN S, ROBERTS S. Deeplob: Deep convolutional neural networks for limit order books[J]. IEEE Transactions on Signal Processing, 2019,67(11): 3001-3012.
|
[27] |
LIU G, MAO Y Z, SUN Q, et al. Multi-scale two-way deep neural network for stock trend prediction[C]// Proceedings of the 29th International Joint Conference on Artificial Intelligence. 2020:4555-4561.
|