Loss Prevention in Stock Marketing

Authors: Shaik Hussain Vali, Doddagalla Deepa, Bommireddy Adi Shankar Reddy, Chinthakayala Nandini, Yedurumidde Lingeswari, K.Himaja

Country: India

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Abstract: 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 using data science techniques. 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.In this Paper for predicting the stock price will use machine learning techniques such as ARIMA, Linear Regression and Random Forest classifier’s.

Keywords: exchange, prediction Random forest support vector machine, Regression.

Paper Id: 232344

Published On: 2025-04-03

Published In: Volume 13, Issue 2, March-April 2025

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