I want to determine if there is any seasonality to the set. The AutoCorrelation shows no spikes but I think it's a little goofy because the data has such a strong upward trend. I'd like to control that upward trend (ie inflation) and then look for seasonality.
You can control for the upward trend by differencing. This is easily handled in the Time Series Analysis module by selecting the difference option. Or, if you prefer, you can calculate the difference yourself by creaing a new column and entering the formula food - foodRow()-1 (i.e., Row()-1 is subscripted). This subtracts the previous observation from the current observation, which removes trends. The first row wild return an error; just click OK.
The differenced values should no longer have an upward trend.
Fit model can be used with an overall trend data as a fcn of time, (maybe time^2)
With the strong overall trend defined, you can find a function for the overall (linear, polynomial etc) using fit model, then "save residuals"- and examine the residuals by month independent of the trend.
I have seen approaches (sometimes used when time series are not regular steps), of calculating the "phase" of the year in radians (month / 6 * pi), calculating a SIN and COS of this phase, and adding them as terms in the fit model.