Abstract
This paper studies how an optimal Neural Network (NN) can be selected that is later used for constructing the highest quality delta-based Prediction Intervals (PIs). It is argued that traditional assessment criteria, including RMSE, MAPE, BIC, and AIC, are not the most appropriate tools for selecting NNs from a PI-based perspective. A new NN model selection criterion is proposed using the specific features of the delta method. Using two synthetic and real case studies, it is demonstrated that this criterion outperforms all traditional model selection criteria in terms of picking the most appropriate NN. NNs selected using this criterion generate high quality PIs evaluated by their length and coverage probability.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.