In this video, learn about the root mean square error (RMSE).
- [Instructor] When you look for ways to express…the degree of accuracy in a forecast,…you're looking for a single number that will show…how closely all of your forecasts estimate…all of your actuals.…As a practical matter, a good forecast results…in small deviations from the actual observations.…So the first solution that might occur to you…is to subtract each forecast from its associated…actual observation, which results in a deviation.…If you do the same with all your forecasts…and observations, you wind up with a set of deviations…as shown in this worksheet in column E.…
You could total or average those deviations…to come up with the single number that expresses…the degree of accuracy in forecasts as show in cell F2.…At first blush, the smaller the total or average deviation…the more accurate the forecasts.…Unfortunately, there's a strong tendency…for some of your deviations to be positive numbers…and some of your deviations to be negative numbers.…It's conceivable that you might have some…large positive deviations and some…
- Demonstrate how to evaluate a baseline using a correlogram.
- Identify the drawbacks of using Microsoft Excel’s exponential smoothing tool.
- Explain the different ways you can initialize the first forecast.
- Compare the average raw deviation forecast with the mean absolute deviation forecast method.
- Break down the reasons to use R instead of Excel for exponential smoothing.