From 2016 Mastering JMP session by Jian Cao @jiancao.View the videos See also JMP 15 Time Series Forecasting videos and resources.
See how to:
Use Time Series Overview and ARIMA Models
- Use Box-Jenkins Methodology
- Build ARIMA (AutoRegressive Integrated Moving-Average) models
- 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
- 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).
