Stock Price Prediction using ARIMA Model

Figure 1: Dataset head
Figure 2: Autocorrelation plot using a Lag of 5
Figure 3: Graphical Representation of Train/Test Split
Figure 4: SMAPE (Symmetric mean absolute percentage error) [2]
  • p is the order of the autoregressive model(number of time lags)
  • d is the degree of differencing (number of times the data have had past values subtracted)
  • q is the order of moving average model. Before building an ARIMA model, we have to make sure our data is stationary.
Figure 5: TATAGLOBAL Price Prediction
Figure 6: Prediction vs Actual Price Comparison

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