From the course: Business Analytics: Forecasting with Seasonal Baseline Smoothing

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- [Conrad] There are plenty of features used by different smoothing models that this course hasn't had enough time to explore even briefly, let alone in depth. If you intend to look further into smoothing as an approach to forecasting, I urge you to examine a model not only from the point of view of the summary statistics provided by functions in R, but also the period to period details that show most clearly in an Excel worksheet. There are so many choices involved in specifying a smoothing model that it can be very difficult to distinguish a change in the nature of the baseline from an apparently minor change in the way a model is specified. Those choices can involve differences between trended and seasonal models, add a difference in small duplicative models, whether a damping parameter has been added to the equation and which error measure has been chosen to evaluate the smoothing constants. With that in mind, I encourage you to explore some of my other courses in this library.

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