Tips on Time Series Forecasting- Python

Question

I'm a newbie when it comes to time series forecasting. I was given this problem a while back:

import pandas as pd
revenue_forecasting_df = pd.DataFrame({'Airing Month' : ['March 2017', 'March 2017', 'March 2017', 'March 2017', 'March 2017', 'March 2017',
                                                         'March 2017', 'March 2017', 'March 2017', 'March 2017', 'March 2017', 'March 2017',
                                                        'April 2017', 'April 2017'],
                                       'Booking Month': ['April 2016', 'May 2016', 'June 2016', 'July 2016', 'August 2016', 'September 2016', 'October 2016',
                                                        'November 2016', 'December 2016', 'January 2017', 'February 2017', 'March 2017', 'May 2016', 'June 2016'],
                                       'Cumulative Revenue': [4876, 36779, 56670, 176273, 343924, 513577, 740029, 971486, 1193934, 1519098, 1863281,2148405, 46507,63985
]})

Basically, I have three columns: the Airing Month, the Booking Month, and Cumulative Revenue. This problem is based on a situation in the radio industry.

Booking Month: The month that the ad was booked Airing Month: The month that the ad was aired Cumulative Revenue: The amount of revenue accumulated cumulative

Basically, I'm trying to figure out how to forecasting revenue for future months based on historical data. Looking online, I've been seeing time series forecasting articles where datasets have only date column and an amount field or a sales field where the first thing to do is to use the date column as an index.

But I, for the life of me, cannot find articles where two dates are given in a dataset. So I'm not sure how to approach those type of problems. Should I use the Booking Month as my index or the Airing Month as my index? I don't really know.

Any tips for how to approach this type of problem would be greatly appreciated. Again, total newb when it comes to time series so please bare with me.