Two-phase hybridisation using deep learning and evolutionary algorithms for stock market forecasting Online publication date: Fri, 07-Jan-2022
by Raghavendra Kumar; Pardeep Kumar; Yugal Kumar
International Journal of Grid and Utility Computing (IJGUC), Vol. 12, No. 5/6, 2021
Abstract: In this paper, a two-phase hybrid model is proposed for stock market forecasting using deep learning approach and evolutionary algorithms. In the first phase of hybridisation, Auto Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) are combined to compose linear and non-linear features of the data set. In the second phase, an improved Artificial Bee Colony (ABC) algorithm using Differential Evolution (DE) is used for the hyperparameter selection of proposed hybrid LSTM-ARIMA model. In this paper, experiments are performed over 10 years of the data sets of Oil Drilling & Exploration and Refineries sector of National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) from 1 September 2010 to 31 August 2020. Obtained result demonstrates that the proposed LSTM-ARIMA hybrid model with improved ABC algorithm has superior performance than its counterparts ARIMA, LSTM and hybrid ARIMA-LSTM benchmark models.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Grid and Utility Computing (IJGUC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook