Abstract Analysis of high‐resolution data offers greater opportunity to understand the nature
of data variability, behaviours, trends and to detect small changes. Climate studies often …

We present ensemble Bayesian model averaging (EBMA) and illustrate its ability to aid
scholars in the social sciences to make more accurate forecasts of future events. In essence …

The geo-statistical Kriging method is conventionally used in the spatial dimension to predict
missing values in a series by utilizing information from neighbouring data, supported by the …

Forecasts of the trajectory of an infectious agent can help guide public health decision
making. A traditional approach to forecasting fits a computational model to structured data …

The collection of reliable and high-quality data is seen as a prerequisite for effective and
efficient rail infrastructure and rolling stock asset management to meet the requirements of …

In developing regions missing data are prevalent in historical hydrological datasets, owing
to financial, institutional, operational and technical challenges. If not tackled, these data …

This work proposes a fault detection and imputation scheme for a fleet of small-scale
photovoltaic (PV) systems, where the captured data includes unlabeled faults. On-site …

We consider ensemble Bayesian model averaging (EBMA) in the context of small-n
prediction tasks in the presence of large numbers of component models. With large numbers …

Aim of this work is to present the preliminary results obtained using Generative Adversarial
Imputation Networks (GAIN) to face the problem of incomplete time series in high frequency …

This research introduces a support tool for the demand forecast management of local
pharmacies. It is based on the forecast of the requirements obtained through the …