Stock market prediction or forecasting is a serious challenge for corporate brokers stakeholders and investors. The stock of each company fluctuates in a random manner and becomes hard and risky for investors to invest. In this project, we apply knowledge of machine learning techniques to predict the stock prices of a particular company. The stock market is so volatile and investors don’t want to fall at risk. So an approach with adequate expertise is designed to help investors to ascertain veiled patterns from the historic data that have the feasible predictive ability in their investment decisions. The historical data has a significant role in, helping the investing people to get an overview of the market behavior during the past decade.We have used ARIMA model to forecast stock price for short term.
Trends of close prices with diffrent shifts
| Week Days | Monthly |
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| Quarterly trend | Yearly trend |
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Residual Dickey-Fuller Test
AutoCorrelation and Partial AutoCorrelation
ARIMA Model with p3 and q2
Forecasting stock price on test data
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