Transform data using differencing, to compute the differences between consecutive observations, to make the time series stationary so that it can be modeled and used for forecasting
Save table that includes the forecasted data
Use Seasonal ARIMA
Build seasonal ARIMA models to handle season components, data that has increasing variability over time or data with a growth pattern that requires data transformation
Specify seasonal autoregressive order, seasonal differencing order, seasonal moving average order and the number of periods per season.
Use Transfer Function and X-11 Decomposition Models
Understand how transfer functions are used to take into account an intervention, such as a policy change or marketing effort, that occurs during the time series during which the model will be built. T
Model and forecast where the intervention is shown as binary (1 for Yes, 0 for No)
Dynamically modify the forecast based on input changes
UseX-11 Decomposition to remove trend and seasonal effects using the X-11 method developed by the US Bureau of the Census
Understand smoothing methods for quick forecasting available in JMP (simple exponential, double exponential, linear exponential, damped-trend linear exponential, seasonal exponential, and Winters method).