Hi Paul,
Currently it does not provide prediction intervals, as it is not assuming a
generative model or a particular error distribution.
I think the best way forward, with nnfor, is to construct empirical ones.
Have a look at this paper for some relatively straightforward approaches that
work quite well under a variety of conditions.
The paper looks at safety stocks, but the same approach can be used for
generating prediction
intervals.https://kourentzes.com/forecasting/2018/06/20/empirical-safety-stock-estimation-based-on-kernel-and-garch-models/
Best,Nikos
Professor, Dept. Management Science
Lancaster University Management School, UK
Centre for Marketing Analytics & Forecasting
blog: http://nikolaos.kourentzes.com
twitter:?@nkourentz
Book: Ord, Fildes & Kourentzes (2017) Principles of Business Forecasting
(2nd ed.), Wessex.
On Tuesday, August 27, 2019, 3:44:45 PM GMT+3, Paul Bernal <paulbernal07
at gmail.com> wrote:
Dear friends,
Hope you are all doing well. I am currently using function mlp (to fit multiple
layer percentron model) to generate forecasts using package nnfor.
I would like to know if the mlp function provides, or is there a way to
construct confidence intervals for the forecasts generated by this mlp function.
Any help and/or? guidance would be much appreciated,
Best regards,
Paul
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