Forecast combinations have flourished remarkably in the forecasting community and, in
recent years, have become part of mainstream forecasting research and activities …

Distributional regression models that overcome the traditional focus on relating the
conditional mean of the response to explanatory variables and instead target either the …

We study whether news and sentiment about bitcoin regulation, the hacking of bitcoin
exchanges and scheduled macroeconomic news announcements affect the volatility of …

Digital soil map** (DSM) techniques have provided soil information that has
revolutionized soil management across multiple spatial extents and scales. DSM …

The goal of quantile regression is to estimate conditional quantiles for specified values of
quantile probability using linear or nonlinear regression equations. These estimates are …

Spatiotemporal problems are ubiquitous and of vital importance in many research fields.
Despite the potential already demonstrated by deep learning methods in modeling …

Alle im vorigen Kapitel beschriebenen Problemstellungen besitzen eine wesentliche
Gemeinsamkeit: Eigenschaften einer Zielvariablen y sollen in Abhängigkeit von Kovariablen …

With the increasing utilization of offshore wind power, accurate prediction of offshore wind
power is crucial for preventive control and scheduling. In this paper, a new hybrid probability …

In this article, we propose two important measures, quantile correlation (QCOR) and quantile
partial correlation (QPCOR). We then apply them to quantile autoregressive (QAR) models …

Recent interest in modern regression modelling has focused on extending available (mean)
regression models by describing more general properties of the response distribution. An …