Implementing a Hybrid Bilst-GRU Model for Stock Price Prediction

Abstract

Predicting stock prices with any degree of accuracy has always fascinated and encouraged
scholarly curiosity in the financial industry. To make educated decisions regarding purchasing, selling, or
holding stocks, investors, traders, and financial institutions greatly value accurate stock price forecasts.
Traditional stock prediction relies on a number of factors that are susceptible to changes in economic data,
investor sentiment, market trends, company fundamentals, and the emotional tone of news reports. To
circumvent these issues, this research recommends a hybrid BiLSTM-GRU model for stock price forecasting.
Data preparation is the first step and include dealing with missing values and using robust data cleaning
procedures to ensure the information is correct and dependable. The next stage, feature engineering, aims to
enhance the model's ability to identify intricate stock market trends and patterns by the extraction of valuable
features from the raw data.When assessing the model's efficacy, the train-test split plays a significant role.
We use normalization techniques to standardize the data so that all characteristics scale uniformly and the
model performs as well as it can. Finally, two classification models, BILSTM and GRU, will be used to model
sequential data using deep learning. By feeding these models the characteristics that have already been
analyzed and developed, we can anticipate how the stock price will move. To carry out this project, the
programming language Python is used.