Stock price prediction with recurrent neural networks approach in deep learning

Time series forecasting is an analysis and modeling approach that aims to make predictions about future events or values by arranging historical data points in time order. This method is used in many fields such as statistics, econometrics, finance, and meteorology. In recent years, with the rise of new methodologies such as machine learning and deep learning, neural networks have become an important tool in financial forecasting. This study uses recurrent neural network architectures to predict financial stock data. Neural networks stand out for their ability to capture complex and non-linear relationships and offer some advantages thanks to their ability to handle incomplete data. This study focuses on the comparison of models built with neural networks with classical stochastic time series methods and emphasizes the potential advantages of neural networks in financial forecasting. Going beyond traditional methods, neural networks can offer the ability to better understand the complexity and dynamics of financial market data and make more accurate predictions. This study explores how neural networks can be used in financial forecasting, offering a new perspective for financial analysts and researchers. Keywords: Time Series Forecasting, Recurrent Neural Networks, Deep Learning.