Effective Stock Market Pricing Prediction Using Long Short Term Memory-Upgraded Model (LSTM-UP) on Evolving Data Sets

Author:

Balamohan S.,Khanaa V.,Sivaraman K.

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Networks and Communications,Computer Graphics and Computer-Aided Design,Computational Theory and Mathematics,Artificial Intelligence,General Computer Science

Reference15 articles.

1. Refenes AN, Zapranis A, Francis G. Stock performance modeling using neural networks: a comparative study with regression models. Neural Net. 1994;7(2):375–88.

2. Vishwanath R, Krishnamurti C. Investment management: a modern guide to security analysis and stock selection. Berlin Heidelberg: Springer- Verlag; 2009.

3. H. G. J. H. Yuling Lin, "An SVM-based approach for stock market trend prediction," in Proceedings of the International Joint Conference on Neural Networks, Dallas, TX, 2013.

4. Bao W, Yue J, Rao Y. A deep learning framework for financial time series using stacked autoencoders and long short term memory. PLOS ONE. 2017;12:7.

5. Qin J, Wang B, Zhou C. Forecasting stock prices with long short term memory neural network based on attention mechanism. PLOS ONE. 2020;20:20.

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