Elina Gaile-Sarkane’s research while affiliated with Riga Technical University and other places

The paper presents a case study on customer behaviour forecast within a parking products domain. In particular, authors compare different LSTM-based DL networks with and without data preprocessing to decide which of the selected architectures and hyperparameter combinations provide the least square error estimate. Unfortunately, well-known forecast methods like regression and ARIMA did not deliver the needs forecast reliability, which brought the authors to the application of DL models. While the paper does not offer novel DL models or architectures, it provides a convenient application insight enabling a better understanding of the application of the selected models. Data has been collected for several months and offers a good study backbone.

The article gives an insight in the economic impact of a higher educational institution (Riga Technical University, Latvia) on the national economy. The role of universities is changing through decades and the number of ways to evaluate university impact increases. Today, tools for the evaluation of higher educational institutions are mainly related to typology and rankings. This research is devoted to finding out the possible ways for calculation which allows to evaluate the added value of higher education and science in the national economy. Results were achieved by choosing a methodology for the evaluation of higher educational institution impact on the national economy. Calculations were made on the example of Riga Technical University and new ways of adjustment of the calculations and the methodology were discovered. The authors concluded that in 2019 one euro invested in Riga Technical University returned to the national economy in the amount of 10.8 euros