Predicting the outcome of various phenomena has always been an attractive research topic in a large number of scientific disciplines, especially in economics. As a scientific discipline, econometrics provides various models for predicting indicators such as GDP, inflation rate, interest rate, price of various goods and services, as well as many others at both micro and macro levels. The development of information technologies has made computational operations much faster and more precisely. However, a special contribution is reflected in the application of data mining for the purpose of extracting relevant information from a large data set. Models developed using data mining provide good results in predicting economic indicators, often more successfully than certain econometric models. This paper aims to forecast the growth of GDP through the application of time series mining on the example of the Republic of Serbia. The analysis was performed in two cases: in the first one models include independent attributes that additionally describe the dependent variable, while in the other case they do not contain these attributes. Three different mining methods were used in both cases (linear regression, multilayer perceptron and random forest) and the obtained results of model validation were presented and interpreted.

There has been a notable surge in scholarly interest in public-private partnerships (PPP), their forms, models and role in the development of social infrastructure. The growing body of research on PPPs makes bibliometric analysis in this area a pertinent task. This study focuses on the literature on PPPs published between 2000 and 2021 and indexed by Scopus, Google Scholar, and Crossref. The study examines the evolution and structure of publication activity and trends in co-authorship as well as reciprocal citation relationships between articles. The top cited publications, authors and journals are identified. We have also conducted a semantic analysis of publication titles and keyword co-occurrence relationships, building a co-occurrence map in VOSviewer software. These research results can be of interest to scholars and practitioners in the field of public-private partnerships.