  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 and approaches to their classification. The main
goal of the study was to develop a typology that will help to improve our
knowledge and understanding of metrics and facilitate their selection in
machine learning regression, forecasting and prognostics. Based on the analysis
of the structure of numerous performance metrics, we propose a framework of
metrics which includes four (4) categories: primary metrics, extended metrics,
composite metrics, and hybrid sets of metrics. The paper identified three (3)
key components (dimensions) that determine the structure and properties of
primary metrics: method of determining point distance, method of normalization,
method of aggregation of point distances over a data set.
