Lecture tutorial - A Brief Tutorial On Exponential...

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A Brief Tutorial On Exponential Smoothing Models Major Observation : Exponential smoothing models are special cases of Box-Jenkins models. When adopting exponential smoothing models for forecasting you are implicitly claiming that the same Box-Jenkins model fits all time series equally well. In many instances, exponential smoothing methods may forecast well, but there may also be many instances where building a general Box-Jenkins model for forecasting would do even better. If one only has a few observations to begin with (say 10 or less) then an ad hoc choice of the smoothing parameter ( α ) (say α = 0.3) can at least generate forecasts whereas having less than 10 observations makes it difficult to use the Box-Jenkins method for building an adequate forecasting model. One has to wait on more observations before progressing from the exponential smoothing models to Box-Jenkins models. 1. The Single Exponential Smoothing (SES) Model is equivalent to a ARIMA(0,1,1) Box-Jenkins
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  • Fall '13
  • TomFomby
  • Time series analysis, single exponential smoothing, exponential smoothing models

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