Passenger Forecasting

Mark's strategy is a good one. After you try each trend line, Have it display the equation and the correlation on the chart. The closer the correlation is to 1, the better it fits your data. When you find the trending line that best fits your data or when there's a diminishing returns on making your formula more complicated (e.g. in my case going to a fourth degree polynomial raises the correlation coefficient slightly), you can take that formula back and input it into a cell.

I enclosed a random example in which I found a 3 degree polynomial best fit the data entered with a correlation of 0.9928.

ChemistB

One other thing to look for is what is driving your data?
My previous post largely assumed that you are simply forecasting pasenger numbers against date.
However, you might get a better correlation against ticket price, or recent air related accidents, or seat pitch, or...
This might also mean that a particular fit might only work for a particular segment of the data.
You might have a lovely fit upto a given date, then a totally different fit afterward (9/11 would be an obvious example of such a cusp point).
It is possible to build complex formulas, to allow for any number of driving critera, but then you have to start forecasting the driving critera into the future...
Mark.

Get 20 years of prior data.