From the course: Business Analytics: Forecasting with Exponential Smoothing
Join today to access over 22,600 courses taught by industry experts or purchase this course individually.
Exponentially declining influence of observations
From the course: Business Analytics: Forecasting with Exponential Smoothing
Exponentially declining influence of observations
- [Instructor] We can get some insight into the reasons that this technique is called exponential smoothing by doing some more manipulating with the basic smoothing formula. We start with the formula for the forecast for time four y hat sub four that's shown using the smoothing form which returns the weighted sum of the prior observation and the prior forecast. Next, have a look at the forecast equation for time three or y hat sub three. It uses the same form as the first equation, but it moves the observation and the forecasts one time period back. At this point, we can remove y hat sub three from the first equation and replace it with the expression to the right of the equal sign in the second equation. The result is the third equation on the screen. It is identical to the first equation except that y hat sub three has been replaced with a calculation of y hat sub three from the second equation. So, where did this first forecast baseline come from? Well, exponential smoothing bases…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.