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Stock-Market Forecasting Using Machine Learning

Author: 
Parag Hirulkar, Dipak Raut, Ashok Shinde, Anjali Jagtap and Smita Kadam
Subject Area: 
Physical Sciences and Engineering
Abstract: 

Stock market process is fully uncertainty affected by many factor so that's anyone cannot predict stock market future to solve this problem construct the model which is gather all information from historical data, analyze data according stock that predict stock market future hence stock market prediction is important extraction in finance and business. Examines the theory and practice of real time analysis techniques for prediction of stock price trend by using a transformed data set in ordinal data format. The original pre transformed data source contains data of heterogeneous data types used for handling of currency values and financial ratios. The data formats in currency values and financial ratios provide a process for computation of stock prices. The transformed data set contains only a standardized ordinal data type which provides a process to measure rankings of stock price trends. The outcomes of both processes are examined and appraised. The primary design is based on time series analysis from machine learning software. This project focuses on using univariate time series forecasting methods for the stock market index, Standard Poor's 500 (abbreviated commonly as SP 500, which is the notation used in this project) emphasizing on Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) modelling. Time series analysis was through using R and R studio to both predict and visualize predictions. Along with the interactivity of plotly through the ggplot2 package we were able to create stunning visuals that help in understanding which time series forecasting method is most appropriate for time series analysis.

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