Stock-Price-Prediction-using-integrated-dashboard-in-R

Following mentioned are some of the key points related to the project which one should know before working on project:

    -The code is done in 'R' Language.
    -There is no need of any external Dataset file, you just need to add the path of website.
    -Enter the path of your website from which you refer database(i.e. i personally used 'https://in.finance.yahoo.com/')
    -Execute each and every line step by step.
     (there is no need to change any line its completely working)
    -UI is also integrated in this code which shows you a good visual interface

Introduction

Stock Market prediction and analysis is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. Stock market is the important part of economy of the country and plays a vital role in the growth of the industry and commerce of the country that eventually affects the economy of the country. Both investors and industry are involved in stock market and wants to know whether some stock will rise or fall over certain period of time. The stock market is the primary source for any company to raise funds for business expansions. It is based on the concept of demand and supply. If the demand for a company's stock is higher, then the company share price increases and if the demand for company's stock is low then the company share price decrease.

Objective Behind Developing this project

    1. To identify factors affecting share market
    2. To generate the pattern from large set of data of stock market for prediction of NEPSE
    3. To predict an approximate value of share price
    4. To provide analysis for users through web application

Models and Functions used

    1. ARIMA Model
    2. Time Series Function
    3. Data Frame Function
    4. Dashboard Function
    5. Plot Function

Algorithm Used ARIMA Model for Forecasting

    ARIMA model works in three blocks as follows
      • AR: Autoregression. A model that uses the dependent relationship between an observation and some number of lagged observations.
      • I: Integrated. The use of differencing of raw observations (i.e. subtracting an observation from an observation at the previous time step) in order to make the time series stationary.
      • MA: Moving Average. A model that uses the dependency between an observation and residual errors from a moving average model applied to lagged observations.
    Then the plots will be displayed on the dashboard in different formats.

System Features

1.Stock Market Prediction

Stock price movements are in somewhat repetitive in nature in the time series of stock values. The prediction feature of this system tries to predict the stock return in the time series value by Network which involves producing an output and correcting the error.

2.Market Analysis

A detailed analysis of Stock market is presented to the user. The analysis contains the performance of most of the listed companies for certain interval of days. The numbers and figures are represented in graphs and plots in the form of line charts.