Improved big data stock index prediction using deep learning with CNN and GRU Online publication date: Wed, 31-Mar-2021
by Jithin Eapen; Abhishek Verma; Doina Bein
International Journal of Big Data Intelligence (IJBDI), Vol. 7, No. 4, 2020
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.
Online publication date: Wed, 31-Mar-2021
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