Data Science, Timeseries, Time Series Analysis, Arima Model, Forecasting.
forecast_steps = 30 forecast = model_fit.forecast(steps=forecast_steps) # Plot the original data and the forecasted values plt.figure(figsize=(10, 5)) plt.plot(t, data, label='Original Data') plt.plot(np.arange(n, n + forecast_steps), forecast, label='Forecasted Values', color='red')