Deep learning has recently received growing interest and attention. It has been successfully applied to many fields. Stock market time-series forecasting is one the most challenging problems for a variety of learning methodologies. In this paper, we studied the integration of deep learning methodologies into stock market forecasting. We evaluated and compared a number of variants of Deep Recurrent Neural Network based on LSTM and GRU. Both bidirectional and unidirectional stacked architectures with multivariate inputs were employed to perform short- and long-term forecasting. The deep learning architectures were also compared to shallow neural networks using S P500 index historical data. It has been noticed that a stacked LSTM architecture has demonstrated the highest forecasting performance for both short- and long-term.
|Title of host publication||21st Saudi Computer Society National Computer Conference, NCC 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - 27 Dec 2018|
|Name||21st Saudi Computer Society National Computer Conference, NCC 2018|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT The authors would like to thank King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, for the support during this work.
© 2018 IEEE.
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Information Systems
- Health Informatics