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Quote from From Bowerman and O'Connell, "Forecasting and Time Series: an Applied Approach", 1993, 3rd edition, page 324:
"...the authors feel that dummy variable regression (and other methods presented later) are usually superior to trigonometric models for modeling seasonal variation. This is because dummy variable models (and other techniques) use a different parameter to model the effect of each different season in a year".
The "other methods" they refer to are formal time series model, like ARIMA. JMP has the capability to fit time series models, but they can be quite complicated. I'd try the dummy variable method first, then venture into ARIMA models if you need more complexity.