Overview
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Learn about a forecasting technique that recognizes and accounts for trends in a baseline, as well as how to run the trend forecast analysis in R.
Syllabus
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Introduction
- Why trended baseline smoothing will help your regression
- Software setup
- A review of SES with a stationary baseline
- Problems using SES with a trended baseline
- Forecasting differences
- Using R for SES
- Using ARIMA(0,1,1) for SES
- Distinguish between a level component and a trend component
- The trend constant compared to the level constant
- Compare smoothing and error correction forms
- Initialize the trend forecasts
- Build the full worksheet and optimize with Solver
- Prepare for analysis with R
- Run and interpret the analysis in R
- Next steps
