فوژان فهيم زاده

Price prediction is super challenging due to the nonlinearity, complexity, an‎d high noise present in financial time series. In the research, we are focusing on addressing this issue to enhance prediction performance in the complex stock market environment. To achieve this, we are proposing a novel integrated approach that utilizes Machine Learning techniques to simultaneously improve the fitting an‎d accuracy of stock price predictions. Additionally, to avoid the problem of overfitting an‎d further boost predictive performance, we are incorporating various ML algorithms for optimization. This study aims to leverage these advanced techniques to make more reliable an‎d accurate stock market predictions.

Lstm , Machine learning , Hyperparameter