Advanced Stock Price Forecasting Using a 1D-CNN-GRU-LSTM Model

This article proposes a novel hybrid network integrating three distinct architectures -CNN, GRU, and LSTM- to predict stock price movements. Here with Combining Feature Extraction and Sequence Learning and Complementary Strengths can Improved Predictive Performance. CNNs can effectively identify short-term dependencies and relevant features in time series, such as trends or spikes in stock prices. GRUs designed to handle sequential data. They are particularly useful for capturing dependencies over time while being computationally less expensive than LSTMs. In the hybrid model, GRUs help maintain relevant historical information in the sequence without suffering from vanishing gradient problems, making them more efficient for long sequences. LSTMs excel at learning long-term dependencies in sequential data, thanks to their memory cell structure. By retaining information over longer periods, LSTMs in the hybrid model ensure that important trends over time are not lost, providing a deeper understanding of the time series data. The novelty of the 1D-CNN-GRU-LSTM hybrid model lies in its ability to simultaneously capture short-term patterns and long-term dependencies in time series data, offering a more nuanced and accurate prediction of stock prices. The data set comprises technical indicators, sentiment analysis, and various aspects derived from pertinent tweets. Stock price movement is categorized into three categories: Rise, Fall, and Stable. Evaluation of this model on five years of transaction data demonstrates its capability to forecast stock price movements with an accuracy of 0.93717. The improvement of proposed hybrid model for stock movement prediction over existing models is 12% for accuracy and F1-score metrics.

Advanced Stock Price Forecasting Using a 1D-CNN-GRU-LSTM Model Keywords:

Hybrid deep neural network , 1D-CNN-GRU-LSTM network , Stock price movement forecasting , Tweet sentiment analysis , Technical indicators