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Rob Hyndman is a Professor of Statistics at Monash University, Australia. He is Editor-in-Chief of the International Journal of Forecasting and author of over 100 research papers in statistical science. He also maintains an active consulting practice, assisting hundreds of companies and organizations. His recent consulting work has involved forecasting electricity demand, tourism demand, the Australian government health budget and case volume at a US call centre. Rob J is a DZone MVB and is not an employee of DZone and has posted 43 posts at DZone. You can read more from them at their website. View Full User Profile

ETS Models Now in EViews 8

03.03.2013
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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 Rob J Hyndman, author and DZone MVB. (source)

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