Now showing 1 - 7 of 7
  • Publication
    FINANCIAL FORECASTING USING NEURAL NETWORKS
    (2016-12-10)
    This paper presents an application of neural networks to financial time-series forecasting. No additional indicators, but only the information contained in the sales time series was used to model and forecast stock exchange index. The forecasting is carried out by two different neural network learning algorithms – error backpropagation and Kohonen self-organising maps. The results are presented and their comparative analysis is performed in this article.
  • Publication
    ANALYSIS OF CONTACTLESS RADIO FREQUENCY IDENTIFICATION TECHNOLOGIES AND ITS USAGE IN REZEKNE CITY INFRASTRUCTURE OBJECTS
    (2016-04-20)
    Zalužinskis, Artūrs
    ;
    Author made the analysis of usage of contactless radio frequency identification technologies in Rezekne city infrastructure objects. Author provides the main types of radio frequency hardware, working principles and worldwide usage statistics. In the conclusion of the paper, author describes the further development opportunities of the radio frequency identification technologies.
  • Publication
    SORTING ALGORITHMS REALIZATION AND THEIR FEATURES
    (2022-05-07)
    Ivars Cabuļevs
    ;
    The performance of computers and programs is the most important thing for a user nowadays. Sorting algorithms appeared in the 19th century, but nowadays, developers often forget about the effectiveness of these algorithms and always use only a couple of algorithms, which are not always the best solution for certain tasks. This slows down the performance of certain important applications for professionals as well as for ordinary users. In this work was told and implemented different numerical sorting algorithms. The development environment here was “Microsoft Visual Studio 2017”. Was used the C++ programming language. Knowledge of sorting algorithms will always help you to optimize your program (if there used sorting), which will have a positive impact on user feedback about your application also this knowledge has a positive effect on the thought processes, allowing you to make the right decisions in the shortest time.
  • Publication
    DATA PREPROCESSING METHODS FOR INTERVAL BASED NEURAL NETWORK PREDICTION
    The paper examines a task of forecasting stock prices of Riga Stock exchange by the use of interval value prediction approach, which is carried out by modified Kohonen neural network learning algorithm. The data preprocessing methods are analyzed and implemented here to solve stock prices prediction task. The proposed data preprocessing methods has been experimentally tested with two types of artificial neural networks.
  • Publication
    RETAIL TURNOVER PREDICTION USING MODULAR ARTIFICIAL NEURAL NETWORKS
    The paper focuses on the retail turnover prediction with artificial neural networks. The artificial neural networks have the potential to learn complex, non-linear relationships within data. The main disadvantage is that neural networks are “black boxes”, so the user cannot explain the obtained results and relationships between data. The modular neural networks allow obtaining more appropriate results by splitting the task into subtasks, thus giving the user more information in the output. In many cases an additional advantage of modular neural network is more precise prediction results, which will be shown in the experimental part of this paper.
    Scopus© Citations 1