By: TimManns
Structural models (for example SAS’ unobserved components model (UCM)). These forecast models generally work very well against real world data, can accept multiple input variables, and decompose the...
View ArticleBy: Tom Reilly
Like Tim, I am a software vendor. Our software, Autobox, uses ARIMA and the use of outliers (ie pulse, level shift, local time trends, and seasonal pulses) to build robust models. Most other approaches...
View ArticleBy: Jeff
Hey Tim, Have you found this technique (new to me) comparable (superior) to ARIMA in your applications?
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By: TimManns
Yes, but with some effort (I find UCM’s less ‘plug and play’ . High forcast accuracy was key – a matter of life or death quite literally. I’ve just completed a very short (15 day) off site project...
View ArticleBy: cristian mesiano
A part from SVR (i wrote in my blog two post on such model applied to a slightly different topic) which is very powerful especially for data having high dimensionality, I tried to do some experiment...
View ArticleBy: Sandro Saitta
Thanks for your input! I also obtained good results using Support Vector Regression (SVR). It’s very interesting to read about your applications!
View ArticleBy: Kevin Scott
In revenue management applications where the goal is allocate capacity to different customer segments based on there willingess to pay, we require a forecast of the remaining demand that is yet to...
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