The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Stock prediction using deep learning
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Advances in neural information processing systems 7
Cios, K., & Shields, M. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,059
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212
ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322
Why Does Unsupervised Pre-training Help Deep Learning?
Erhan, D., Bengio, Y., Courville, A. C., Manzagol, P.-A., Vincent, P., & Samy Bengio, A. (2010) · Cited by 2,178
Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.07.040 · Cited by 1,141
Exploring Strategies for Training Deep Neural Networks
Larochelle, H., Bengio, Y., Louradour, J., & Lamblin, P. (2009) · doi:10.1145/1577069.1577070 · Cited by 1,095
Cited by
Variational Quantum Circuits for Deep Reinforcement Learning
Chen, S. Y.-C., Yang, C.-H. H., Qi, J., Chen, P.-Y., Ma, X., & Goan, H.-S. (2020) · IEEE Access · doi:10.1109/access.2020.3010470 · Cited by 452
Shrestha, Y. R., Krishna, V., & von Krogh, G. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.09.068 · Cited by 331
Deep learning of subsurface flow via theory-guided neural network
Wang, N., Zhang, D., Chang, H., & Li, H. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124700 · Cited by 322
Stock Price prediction using LSTM and SVR
Bathla, G. (2020) · 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) · doi:10.1109/pdgc50313.2020.9315800 · Cited by 139
Stock-Price Forecasting Based on XGBoost and LSTM
Hoang Vuong, P., Tan Dat, T., Khoi Mai, T., Hoang Uyen, P., & The Bao, P. (2022) · Computer Systems Science and Engineering · doi:10.32604/csse.2022.017685 · Cited by 100
Metadata
- Authors
- Singh, R., & Srivastava, S.
- Venue
- Multimedia Tools and Applications
- Published
- 2016-12-17
- Volume
- 76
- Issue
- 18
- Pages
- 18569-18584
- ISSN
- 1380-7501 (ISSN-L)
- 1573-7721
- OpenAlex
- W2566564364
- Cited by
- 339
- References
- 33
- FWCI
- 13.50
- Type
- journal article
- Language
- en
- Open access
- closed
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- Singh, R., & Srivastava, S. (2017). Stock prediction using deep learning. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-016-4159-7
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
