Stock Price Prediction Using Deep Learning with GRU (Gated Recurrent Units) and Moving Average Algorithm

A crucial component of financial analysis is stock market prediction, where precise forecasting canassist investors in making well-informed choices and optimizing returns. Numerous factors, including globalevents, economic policies, and market sentiment, all have an impact on the stock market's extreme volatility.Artificial intelligence-based approaches are a promising solution because traditional methods frequently failto capture these intricate dependencies. This project presents a deep learning-based stock prediction systemthat uses the Moving Average and Gated Recurrent Units (GRU) algorithms to predict stock prices withhigh accuracy. The system helps traders, analysts, and researchers reduce financial risks by analyzinghistorical stock data, identifying significant patterns, and forecasting future stock trends. The project uses apotent recurrent neural network (RNN) variant called GRU, which effectively handles sequential stock databy capturing long-term dependencies and lowering computational complexity, in place of traditionalstatistical models. The Moving Average algorithm is used to enhance trend identification and even out stockprice swings. The system ensures current market information by retrieving historical and real-time stockdata from Yahoo Finance (yfinance). To increase prediction accuracy, the model is subjected to datapreprocessing methods such as normalization and error reduction. Because the closing price has a biginfluence on the opening price the next day, it is mostly used as the key feature for prediction. TensorFlowoptimizes the training process, and Mean Absolute Error (MAE) is used to assess performance in order toreduce deviations.