Title: Improved big data stock index prediction using deep learning with CNN and GRU

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.

DOI: 10.1504/IJBDI.2020.113868

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 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article