The Stock Price Forecasting system uses a deep learning model to predict future stock prices based on historical data. It takes in historical price data and technical indicators, preprocesses the data, trains a deep neural network, and generates predictions for future stock prices

Matlab and R functions to estimate the dynamic factor models and to perform forecasting as in Stock and Watson 2002, Forni et al. 2000, Forni et al. 2005, Forni et al. 2017.

Developed a neural network model in MATLAB to forecast Apple stock prices using historical data. Applied Gradient Descent and Quasi-Newton optimization methods to train a single hidden-layer feedforward network with a custom activation function. Integrated data preprocessing, feature normalization, and L2 regularization to reduce overfitting.

Tesla Stock Price Forecasting with Time Series Analysis in MATLAB