Forecasting research trends using population dynamics model with Burgers’ type interaction

The increasing costs of research and the decreasing lifetime of products and processes make the decisions on allocation of R&D funds strategically important. Therefore, ability to predict research trends is crucial in minimizing risks of R&D expenditure planning. The purpose of this paper is to propose a model for efficient prediction of research trends in a chosen branch of science. The approach is based on population dynamics with Burgers’ type global interaction and selective neighborhood. The model is estimated based on a training set. Then, an out-of-sample forecast is performed. The research trends of filtration and rectification processes were analyzed in this paper. The simulation results show that the model is able to predict the trends with a considerable accuracy and should, therefore, be tested on a wider range of research fields.