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Using data from the Bureau of Transportation Statistics ( ) I downloaded US domestic passenger data by year/month from 2005 to January 2015.

Passenger airline data (at least in the US) is seasonal, with high volumes in the summer months.  Given a series of actual / historical data values we can use triple exponential smoothing algorithms to project the data. The airline passenger data are a series of data points over time, giving the phrase time series analysis.


I am using SAP Predictive Expert Analytics.  In the above figure, I have loaded the .CSV file of airline passenger data.


I select R-triple exponential smoothing, because as it shows above it provides “seasonality based time series forecasting”

See R – Forecasting – Training Material

I can drag it on to the workspace.


The above shows I select forecast mode as I want to forecast the data.

The dependent column is the column we wish to forecast, in this case, passenger count

Period for the data is monthly


After running the model, I see success and switch to the Results view.


Above shows the forecast passenger values by month for 2015 and part of 2016.


For fun I also run the SAP-provided triple exponential smoothing (not the R-algorithm)


It provides similar forecast results, with July 2015 being the high month of projected passenger travel in the US.


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