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

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

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 forecas...

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Bibliographic Details
Published in:Mathematics (Basel) Vol. 11; no. 3; p. 590
Main Authors: Zaheer, Shahzad, Anjum, Nadeem, Hussain, Saddam, Algarni, Abeer D., Iqbal, Jawaid, Bourouis, Sami, Ullah, Syed Sajid
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-01-2023
Subjects:
Algorithms
CNN
Data analysis
Deep learning
Food science
Forecasting
LSTM
Mathematical models
Methods
Neural networks
Parameters
Prices
Pricing
RNN
Securities markets
Statelessness
Stock exchanges
Stock price forecasting
stock prices
Time series
Trends
Pakistan
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11030590