prepare train and test sets train_size int(prophet_df shape[ ]* train_df prophet_df ix[:train_sizetest_df prophet_df ix[train_size+ :once we have the datasets preparedwe create an object of the prophet class and simply fit the model using fit(function kindly note that the model expects the time series value to be in column named 'yand timestamp in column named 'dsto make forecastsprophet requires the set of dates for which we need to forecast for thisit provides clean utility called the make_future_dataframe()which takes the number of days required for the forecast as input the following snippet uses this dataframe to forecast values in [ prepare future dataframe test_dates pro_model make_future_dataframe(periods=test_df shape[ ]forecast values forecast_df pro_model predict(test_datesthe output from the predict(function is dataframe that includes both in-sample predictions as well as forecasted values the dataframe also includes the confidence interval values all of this can be easily plotted using the plot(function of the model object the following snippet plots the forecasted values against the original time series along with its confidence intervals in [ plot against true data plt plot(forecast_df yhat, =' ',label='forecast'plt plot(forecast_df yhat_lower iloc[train_size+ :]linestyle='--', =' ',alpha= label='confidence interval'plt plot(forecast_df yhat_upper iloc[train_size+ :]linestyle='--', =' ',alpha= label='confidence interval'plt plot(prophet_df , =' ',label='true data'plt legend(plt title('prophet model forecast against true data'plt show(figure - forecasts from prophet against true/observed values