Time Series Forecasting Based on Deep Learning CNN-LSTM-GRU Model on Stock Prices

Time Series Forecasting Based on Deep Learning CNN-LSTM-GRU Model on Stock Prices

  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-6
Year of Publication : 2023
Author : Ghani Rizky Naufal, Antoni Wibowo
DOI : 10.14445/22315381/IJETT-V71I6P215

How to Cite?

Ghani Rizky Naufal, Antoni Wibowo, "Time Series Forecasting Based on Deep Learning CNN-LSTM-GRU Model on Stock Prices," International Journal of Engineering Trends and Technology, vol. 71, no. 6, pp. 126-133, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I6P215

Abstract
An equity, commonly referred to as a stock, is a financial asset representing partial ownership in the company that issued it. Stocks are favored among investors due to their potential for quick financial gains. It is important to note that stock prices are not arbitrary but instead follow specific patterns and can be analyzed and predicted using discrete time series models. This allows for studying and forecasting stock prices, enabling investors to make informed decisions. Much research has been done to forecast stock prices. The Long Short-Term Memory (LSTM) model, known for its popularity in deep learning, lacks the necessary strength to serve as the main approach for time series forecasting. To address the drawbacks, many various elements employing many methodologies and procedures are addressed; one option is to integrate several deep learning models to create a hybrid deep learning model. Researchers have utilized hybrid deep learning models LSTM-GRU and CNN-LSTM to forecast and analyze time series data, and both models outperform LSTM. The purpose of this research is to create a hybrid deep learning model that combines deep neural network-based models LSTM and Gated Recurrent Units (GRU) with 1D Convolutional Neural Networks (1D-CNN) that focuses on three stock prices, TSLA, GOOG, and TWTR. The findings reveal that the proposed model outperforms the LSTM network and other hybrid models in terms of prediction errors. This shows that the proposed model is effective in forecasting stock prices.

Keywords
Deep Learning, Time series, Forecasting, Stock prices, CNN, LSTM, GRU.

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