Informatica

This paper presents model-based forecasting of the Lithuanian education system in the period of 2001–2010. In order to obtain satisfactory forecasting results, development of models used for these aims should be grounded on some interactive data mining. The process of the development is usually accompanied by the formulation of some assumptions to background methods or models. The accessibility and reliability of data sources should be verified. Special data mining of data sources may verify the assumptions. Interactive data mining of the data, stored in the system of the Lithuanian teachers' database, and that of other sources representing the state of the education system and demographic changes in Lithuania was used. The models cover the estimation of data quality in the databases, analysis of the flow of teachers and pupils, clustering of schools, the model of dynamics of the pedagogical staff and pupils, and the quality analysis of teachers. The main results of forecasting and integrated analysis of the Lithuanian teachers' database with other data reflecting the state of the education system and demographic changes in Lithuania are presented.

The presented article is about a research using artificial neural network (ANN) methods for compound (technical and fundamental) analysis and prognosis of Lithuania's National Stock Exchange (LNSE) indices LITIN, LITIN-A and LITIN-VVP. We employed initial pre-processing (analysis for entropy and correlation) for filtering out model input variables (LNSE indices, macroeconomic indicators, Stock Exchange indices of other countries such as the USA – Dow Jones and S&P, EU – Eurex, Russia – RTS). Investigations for the best approximation and forecasting capabilities were performed using different backpropagation ANN learning algorithms, configurations, iteration numbers, data form-factors, etc. A wide spectrum of different results has shown a high sensitivity to ANN parameters. ANN autoregressive, autoregressive causative and causative trend model performances were compared in the approximation and forecasting by a linear discriminant analysis.