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Time Series Analysis and Forecasting

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. 
  • 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). 
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