Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Performance metrics (error measures) are vital components of the evaluation frameworks in
various fields. The intention of this study was to overview of a variety of performance metrics …

Time series forecasting is a critical aspect of data analysis, with applications ranging from
finance and economics to weather prediction and industrial processes. This review paper …

Aim/Purpose: The aim of this study was to analyze various performance metrics and
approaches to their classification. The main goal of the study was to develop a new typology …

This paper aims at comparing different forecasting strategies combined with the STL
decomposition method. STL is a versatile and robust time series decomposition method. The …

Water reservoirs play a critical role in water resource management systems, serving various
purposes such as water supply, hydropower generation, and flood control. Accurate long …

Accurate and robust forecasting methods for univariate time series are very important when
the objective is to produce estimates for large numbers of time series. In this context, the …

Forecasting is a challenging task that typically requires making assumptions about the
observed data but also the future conditions. Inevitably, any forecasting process will result in …

We introduce a new forecasting methodology, referred to as adaptive learning forecasting,
that allows for both forecast averaging and forecast error learning. We analyze its theoretical …

The accurate prediction of failure events is of central interest to the field of predictive
maintenance, where the role of forecasting is of paramount importance. In this paper, we …