Authors: Jithin Eapen; Abhishek Verma; Doina Bein
Addresses: Department of Computer Science, California State University, Fullerton, California 92834, USA ' Department of Computer Science, New Jersey City University, Jersey City, NJ 07305, USA ' Department of Computer Science, California State University, Fullerton, California 92834, USA
Abstract: Stock index prediction has been a challenging problem due to difficult to model complexities of the stock market. More recently deep learning approaches have become an important method in modelling complex relationships in time-series data. In this paper: we propose novel deep learning models that combine multiple pipelines of convolutional neural network and uni-directional or bi-directional gated recurrent units. Proposed models improve prediction performance and execution time upon previously published models on large scale S&P 500 dataset. We present several variations of multiple and single pipeline deep learning models based on different CNN kernel sizes and number of GRU units.
Keywords: stock prediction; S&P 500; CNN; gated recurrent unit; GRU; deep learning.
International Journal of Big Data Intelligence, 2020 Vol.7 No.4, pp.202 - 210
Received: 24 Nov 2019
Accepted: 18 Sep 2020
Published online: 15 Mar 2021 *