Stock Market Forecasting Technique using Arima Model
Bijesh Dhyani1, Manish Kumar2, Poonam Verma3, Abhisheik Jain4

1Bijesh Dhyani*, Management Studies, Graphic Era Deemed to be University, Dehradun, India.
2Manish Kumar, Management Studies, Graphic Era Deemed to be University, Dehradun, India.
3Poonam Verma, SOC, Grpahic Era Hill University, Dehradun, India.
4Abhishek Jain, SOC, Grpahic Era Hill University, Dehradun, India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 2694-2697 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8405038620/2020©BEIESP | DOI: 10.35940/ijrte.F8405.038620

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Abstract: In Financial market, various shares, bonds, securities or currencies are traded on the daily basis , thus making most of the datasets as time series data where price is plotted against a time series[11] . There are many techniques and analysis technique that can be used with times series data like ARIMA model, exponential Smoothing, Neural Networks or Simple Moving average. However ARIMA Model is commonly used to understand time series analysis in order to extract meaningful characteristics of the data and help in the prediction of the stock prices.[12] since it helps to understand what happened in past and past behavior of data can help to predict future. Time series is a special property and different set of predictive algorithm. There are three variants of the ARIMA Model namely Basic, Trend Based and Wavelet Based. In this paper key components of time series data have been discussed and implemented using ARIMA model for we have collected NIFTY daily data of Nifty50 index and wants to predict future value of the Stock.
Keywords: ARIMA Model, Stock Market, Prices, Time Series data.
Scope of the Article: Agent-based Auctions and Marketplaces.