Abstract

There are several companies in the industry working on cutting-edge algorithms and implementation views for machine learning. Time series forecasting has been widely used to determine the future prices of stock, and the analysis and modelling of finance time series importantly guide investors decisions and trades . This work proposes an intelligent time series prediction system that uses sliding-window optimization for the purpose of predicting the stock prices . The system has a graphical user interface and functions as a stand-alone application. The proposed model is a promising predictive technique for highly non-linear time series, whose patterns are difficult to capture by traditional models. There are several companies in the industry working on cutting-edge algorithms and implementation views for machine learning.Predicting Stock Market has been goal of investors since its existence. Everyday billions of dollars are traded on exchange, and behind each dollar is an investor hoping to profit in one way or another. Entire companies rise and fall daily based on the behavior of the market. Should an investor be able to accurately predict market movements, it offers a tantalizing promises of wealth and influence.