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Time Series Analysis and Forecasting

 

See how to:

• Understand and implement Box-Jenkins Methodology JMP uses for time series analysis
• Build ARIMA (AutoRegressive Integrated Moving-Average) models, determine if they are adequate, compare models and modify models
• Transform data using differencing, a method built into JMP 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

 

 

See how to:

• Build Seasonal ARIMA models that handle season components, data that has increasing variability over time or data with a growth pattern that requires data transformation
• Understand how JMP calculates ARIMA models
• Specify seasonal autoregressive order, seasonal differencing order, seasonal moving average order and the number of periods per season

 

 

See how to:

• Use transfer functions when an intervention, such as a policy change or marketing effort, occurs during the time series during which the model will be built
  • Build an ARIMA-X and ARIMA model that takes into account the intervention
  • Model and forecast where the intervention is shown as binary (1 for Yes, 0 for No)
  • Use Transfer Function options and JMP’s Interactive Forecasting to dynamically modify the forecast based on input changes./li>
• Use X-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)

Resources

• Materials for introducing and applying Time Series concepts. Provided by Professor Bob Stine, Wharton School of Business, University of Pennsylvania.

16-30 min31+ min6-15 minTime SeriesVideo