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
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 <[hidden email]> wrote:
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,
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