Stock Price Forecasting by Time Series Analysis

The technological advancement and mountains of financial data now available, more than ever it is crucial for investors, financial institutions, and decision-makers to make an informed choice by obtaining accurate stock price forecasting. Traditional techniques of forecasting often fail in today's complex market dynamics. This paper reveals a number of techniques of machine learning that can be applied to the field of stock price forecasting, de-emphasizing building up the accuracy and relia- bility of such predictions.[2] The main key algorithms presented are ElasticNet for feature selection and XGBoost, CatBoost, and an ensemble Voting Regressor. Its ability to analyze series data and identify strong, non-trivial patterns from the time series of stock price data made this model the best choice. Results show a performance that indicates just how well the ensemble Voting Regressor performed over the rest, with accuracy 99.6% far beyond any model from the bunch, promising much sharper resolution in financial forecasting. XGBoost and CatBoost also had very comparable accuracy that assured the productive use of machine learning in applying it for financial analytics. The research is not only a glorification of benefits from application of developed models of machine learning for predicting stock prices but also introduces points for further refinement of methods of prediction[4].