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.


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    Title :

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


    Contributors:


    Publication date :

    2016




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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