A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model

Shahzad Zaheer, Nadeem Anjum, Saddam Hussain (), Abeer D. Algarni, Jawaid Iqbal, Sami Bourouis and Syed Sajid Ullah ()
Additional contact information
Shahzad Zaheer: Department of Computer Science, Capital University of Science & Technology, Islamabad 44000, Pakistan
Nadeem Anjum: Department of Software Engineering, Capital University of Science & Technology, Islamabad 44000, Pakistan
Saddam Hussain: School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
Abeer D. Algarni: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Jawaid Iqbal: Department of Software Engineering, Capital University of Science & Technology, Islamabad 44000, Pakistan
Sami Bourouis: Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Syed Sajid Ullah: Department of Information and Communication Technology, University of Agder (UiA), N-4898 Grimstad, Norway

Mathematics, 2023, vol. 11, issue 3, 1-24

Abstract: Financial data are a type of historical time series data that provide a large amount of information that is frequently employed in data analysis tasks. The question of how to forecast stock prices continues to be a topic of interest for both investors and financial professionals. Stock price forecasting is quite challenging because of the significant noise, non-linearity, and volatility of time series data on stock prices. The previous studies focus on a single stock parameter such as close price. A hybrid deep-learning, forecasting model is proposed. The model takes the input stock data and forecasts two stock parameters close price and high price for the next day. The experiments are conducted on the Shanghai Composite Index (000001), and the comparisons have been performed by existing methods. These existing methods are CNN, RNN, LSTM, CNN-RNN, and CNN-LSTM. The generated result shows that CNN performs worst, LSTM outperforms CNN-LSTM, CNN-RNN outperforms CNN-LSTM, CNN-RNN outperforms LSTM, and the suggested single Layer RNN model beats all other models. The proposed single Layer RNN model improves by 2.2%, 0.4%, 0.3%, 0.2%, and 0.1%. The experimental results validate the effectiveness of the proposed model, which will assist investors in increasing their profits by making good decisions.

Keywords: forecasting; deep learning; stock prices; CNN; LSTM; RNN (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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