Data mining workspace as an optimization prediction technique for solving transport problems

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25510%2F16%3A39902072" target="_blank" >RIV/00216275:25510/16:39902072 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.20858/tp.2016.11.3.3" target="_blank" >http://dx.doi.org/10.20858/tp.2016.11.3.3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.20858/tp.2016.11.3.3" target="_blank" >10.20858/tp.2016.11.3.3</a>

Result language
angličtina
Original language name
Data mining workspace as an optimization prediction technique for solving transport problems
Original language description
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.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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