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.

翻译：本文提出对传统回归神经网络（NN）的扩展，用满足所需置信水平的预测区间替代其产生的点预测。我们的方法遵循一种名为共形预测（CP）的新型机器学习框架，该框架在仅假设数据独立同分布（i.i.d.）的条件下，为预测分配可靠的置信度量。我们在四个基准数据集以及跨电离层链路重要参数——总电子含量（TEC）预测问题上评估了所提方法；对于后者，我们使用了跨越11年收集的超过60000个TEC测量值组成的数据集。实验结果表明，该方法生成的预测区间既经过良好校准，又足够紧凑，具有实际应用价值。