@inproceedings{cf8a7d163f4a4ca7809414d0bf878c6f,
title = "Neural Network Prediction Interval Based on Joint Supervision",
abstract = "In this paper, a new prediction interval model based on a joint supervision loss function for capturing the uncertainties associated with the modeled phenomenon is described. This model provides the upper and lower bounds of the predicted values in accordance with the desired coverage probability, as well as their expected values. A benchmark problem is used to evaluate the proposed method, and a comparison with the neural network covariance method is performed. Additionally, the proposed method was applied to forecast the residential demand from a town in UK, considering the prediction interval performance for one-day ahead. The results show that the method is able to generate an interval with narrower width than the covariance method, and maintains the coverage probability. The information provided by the prediction interval could be used in the design of microgrid energy management systems.",
keywords = "Prediction interval, joint supervision, neural network",
author = "Nicol{\'a}s Cruz and Mar{\'i}n, \{Luis G.\} and Doris S{\'a}ez",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Joint Conference on Neural Networks, IJCNN 2018 ; Conference date: 08-07-2018 Through 13-07-2018",
year = "2018",
month = oct,
day = "10",
doi = "10.1109/IJCNN.2018.8489264",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings",
address = "United States",
}