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

Kuptcova, A.; Průša, P.; Federko, G.; Molnár, V.

doi:10.20858/tp.2016.11.3.3

Artykuł - szczegóły

Tytuł artykułu

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

Autorzy

Kuptcova A. , Průša P. , Federko G. , Molnár V.

Treść / Zawartość

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DOI

10.20858/tp.2016.11.3.3

Warianty tytułu

Решение задачи прогнозирования в транспортной отрасли с помощью методов data mining

Języki publikacji

Abstrakty

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.

В данной статье рассматривается задача прогнозирования временных рядов, которая заключается в построении модели для предсказания будущих событий. В исследовании используются методы интеллектуального анализа данных. Модель прогнозирования позволяет адекватно оценивать исследуемый процесс. Целью исследования является изучение динамики расходов при реализации экспортной продукции. Прогнозирование осуществляется с помощью ARIMA-модели, на основе метода экспоненциального сглаживания и по технологии логической нейронной сети. Построение базового и быстрого сценария прогнозирования является важным и ответственным этапом в научной деятельности.

Słowa kluczowe

time series prediction data mining neural network modelling

predykcja szeregów czasowych eksploracja danych sieć neuronowa modelowanie

Wydawca

Wydawnictwo Politechniki Śląskiej

Czasopismo

Transport Problems

Rocznik

2016

Tom

T. 11, z. 3

Strony

21--31

Opis fizyczny

Bibliogr. 36 poz.

Twórcy

autor

Kuptcova A.

University of Pardubice, Jan Perner Transport Faculty, Department of Transport Management, Marketing and Logistics Studentská 95, 532 10 Pardubice, Czech Republic

autor

Průša P.

petr.prusa@upce.cz

University of Pardubice, Jan Perner Transport Faculty, Department of Transport Management, Marketing and Logistics Studentská 95, 532 10 Pardubice, Czech Republic

autor

Federko G.

Technical University of Košice, Faculty BERG, Logistics Institute of Industry and Transport, Park Komenského 14, 043 84 Košice, Slovakia

autor

Molnár V.

Technical University of Košice, Faculty BERG, Logistics Institute of Industry and Transport, Park Komenského 14, 043 84 Košice, Slovakia

Bibliografia

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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).

Typ dokumentu

Bibliografia

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