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.

该论文采用一种名为 Conformal Prediction (CP) 的新型机器学习框架，基于独立同分布的数据，提出了一种扩展传统回归神经网络的方法，将点预测转化为满足所需置信水平的预测区间。作者在四个基准数据集和预测电离层连接中的重要参数之一 —— 总电子含量 (TEC) 的预测问题上进行了实证评估，结果表明该方法产生的预测区间在实践中既具有良好的校准性又足够紧凑。

基于回归神经网络的可靠预测区间

Reliable Prediction Intervals with Regression Neural Networks

Tsunamis can trigger internal gravity waves (IGWs) in the ionosphere,
perturbing the Total Electron Content (TEC) - referred to as Traveling
Ionospheric Disturbances (TIDs) that are detectable through the Global
Navigation Satellite System (GNSS). The GNSS are constellations of satellites
providing signals from Earth orbit - Europe's Galileo, the United States'
Global Positioning System (GPS), Russia's Global'naya Navigatsionnaya
Sputnikovaya Sistema (GLONASS) and China's BeiDou. The real-time detection of
TIDs provides an approach for tsunami detection, enhancing early warning
systems by providing open-ocean coverage in geographic areas not serviceable by
buoy-based warning systems. Large volumes of the GNSS data is leveraged by deep
learning, which effectively handles complex non-linear relationships across
thousands of data streams. We describe a framework leveraging slant total
electron content (sTEC) from the VARION (Variometric Approach for Real-Time
Ionosphere Observation) algorithm by Gramian Angular Difference Fields (from
Computer Vision) and Convolutional Neural Networks (CNNs) to detect TIDs in
near-real-time. Historical data from the 2010 Maule, 2011 Tohoku and the 2012
Haida-Gwaii earthquakes and tsunamis are used in model training, and the
later-occurring 2015 Illapel earthquake and tsunami in Chile for out-of-sample
model validation. Using the experimental framework described in the paper, we
achieved a 91.7% F1 score. Source code is available at:
this https URL Our work represents a new frontier in
detecting tsunami-driven IGWs in open-ocean, dramatically improving the
potential for natural hazards detection for coastal communities.

使用实时检测行程海域重建大气不稳定性（TIDs）的方法提升海啸预警系统并实时检测海啸引发的内部重力波。