This Rshiny app forecasts the stock price change based on previous price data The project was submitted to the university as a part of the end term exam for Econometrics in the Masters of public policy program.
Data: 2013 to 2018
Model: ARIMA(7,1,4)
With this app, we can fit an ARIMA model to the dataset having the stock prices of Google from years 2013-18 .The app takes the following inputs as follows:
1)The category of stock price to be forecasted : Opening price, closing price, etc.
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Choice of Augmented Dickey-Fuller Test for stationarity : Actual, differenced.
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Choice of correlation function : Auto correlation function , Partial correlation function
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The parameters of the ARIMA model :
• p: The number of lag observations included in the model, also called the lag order.
• d: The number of times that the raw observations are differenced, also called the degree of differencing.
• q: The size of the moving average window, also called the order of moving average.
#I used the ARIMA model with the lag value of 7 for autoregression, a difference order of 1 to make the time series stationary, and a moving average model of 4.
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Number of days for which forecasting is to be done
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Choice of Forecasted values or Forecasted plot.