Platinum Partner
architects,bigdata,tool,tools & methods,big data

ETS Models Now in EViews 8

The ETS mod­el­ling frame­work devel­oped in my 2002 IJF paper (with Koehler, Sny­der and Grose), and in my 2008 Springer book (with Koehler, Ord and Sny­der), is now avail­able in EViews 8. I had no idea they were even work­ing on it, so it was quite a sur­prise to be told that EViews now includes ETS models.

Here is the blurb from the release notes:

EViews 8 now offers sup­port for expo­nen­tial smooth­ing using the dynamic non­lin­ear model frame­work of Hyn­d­man, Koehler, et al. (2002).

The ETS (Error-​​Trend-​​Seasonal or Expo­nen­Tial Smooth­ing) frame­work defines an extended class of expo­nen­tial smooth­ing meth­ods that encom­passes stan­dard ES mod­els (e.g., Holt and Holt–Winters addi­tive and mul­ti­plica­tive meth­ods), but offer a vari­ety of new methods.

In addi­tion ETS smooth­ing offers a the­o­ret­i­cal foun­da­tion for analy­sis of these mod­els using state-​​space based like­li­hood cal­cu­la­tions, with sup­port for model selec­tion and cal­cu­la­tion of fore­cast stan­dard errors.

ETS Smoothing

Until now, ETS mod­els have only been avail­able in R (the ets func­tion in the fore­castpack­age). I believe SAS has also been work­ing on includ­ing them, but noth­ing has appeared yet.

Published at DZone with permission of {{ articles[0].authors[0].realName }}, DZone MVB. (source)

Opinions expressed by DZone contributors are their own.

{{ tag }}, {{tag}},

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}
{{ parent.authors[0].realName || parent.author}}

{{ parent.authors[0].tagline || parent.tagline }}

{{ parent.views }} ViewsClicks
Tweet

{{parent.nComments}}