Using data from the Bureau of Transportation Statistics ( http://www.rita.dot.gov/bts/acts ) 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”
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.