Study of Hybrid Deep Learning Model for Stock Price Predictions

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@article{1706494,
      author = {Kyaw Kyaw Khaing},
      title = {Study of Hybrid Deep Learning Model for Stock Price Predictions},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {5},
      pages = {152-163},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706494.pdf},
      abstract = {The prediction of stock prices is a complex and challenging task due to the volatile and non-linear nature of financial markets. Traditional statistical methods often fall short in accurately capturing the intricate patterns within stock price data. In recent years, hybrid deep learning models have emerged as a promising solution. This study reviews current state-of-the-art hybrid models for stock price forecasting, examining combinations of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) models integrated with methods like Support Vector Machines (SVM) and Random Forests (RF). The paper discusses the strengths and limitations of these hybrid models, highlighting their ability to capture both temporal dependencies and complex feature interactions. By providing a comprehensive review of current trends and challenges, this survey aims to guide future research and development in this field.},
      keywords = {Deep Learning, Hybrid Neural Network, Stock Price Prediction},
      month = {November},
  }