Reliable Prediction Intervals with Regression Neural Networks
Harris Papadopoulos Haris Haralambous
This paper proposes an extension to conventional regression Neural Networks
(NNs) for replacing the point predictions they produce with prediction
intervals that satisfy a required level of confidence. Our approach follows a
novel machine learning framework, called Conformal Prediction (CP), for
assigning reliable confidence measures to predictions without assuming anything
more than that the data are independent and identically distributed (i.i.d.).
We evaluate the proposed method on four benchmark datasets and on the problem
of predicting Total Electron Content (TEC), which is an important parameter in
trans-ionospheric links; for the latter we use a dataset of more than 60000 TEC
measurements collected over a period of 11 years. Our experimental results show
that the prediction intervals produced by our method are both well-calibrated
and tight enough to be useful in practice.