DATA MINING WORKSPACE AS AN OPTIMIZATION PREDICTION TECHNIQUE FOR SOLVING TRANSPORT PROBLEMS

• Main Authors: Anastasiia KUPTCOVA, Petr PRŮŠA, Gabriel FEDORKO, Vieroslav MOLNÁR
• Format: Article
• Language: English
• Published: Silesian University of Technology 2016-09-01
• Series: Transport Problems
• Subjects: time-series prediction; data mining; neural network; modelling
• Online Access: http://transportproblems.polsl.pl/pl/Archiwum/2016/zeszyt3/2016t11z3_03.pdf
• ISSN: 1896-0596; 2300-861X

This article addresses the study related to forecasting with an actual high-speed decision making under careful modelling of time series data. The study uses data-mining modelling for algorithmic optimization of transport goals. Our finding brings to the future adequate techniques for the fitting of a prediction model. This model is going to be used for analyses of the future transaction costs in the frontiers of the Czech Republic. Time series prediction methods for the performance of prediction models in the package of Statistics are Exponential, ARIMA and Neural Network approaches. The primary target for a predictive scenario in the data mining workspace is to provide modelling data faster and with more versatility than the other management techniques.