Forecasting, Data Science, Python, Data Visualization, Machine Learning.
fit.forecast(steps = 12) # Plot the data and forecast data['Passengers'].plot(label = 'Observed') forecast.plot(label = 'ARIMA Forecast', color = 'red') plt.legend() plt.title("ARIMA Forecast") plt.xlabel("Year") plt.ylabel("Passengers") plt.show() G2. ARIMA: R library(forecast) library(ggplot2) #