Data Mining Workspace as an Optimization Prediction Technique for Solving Transport Problems

Abstract

Summary. This article addresses the study related to forecasting with an actual highspeed decision making under careful modelling of time series data. The study uses datamining 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. РЕШЕНИЕ ЗАДАЧИ ПРОГНОЗИРОВАНИЯ В ТРАНСПОРТНОЙ ОТРАСЛИ С ПОМОЩЬЮ МЕТОДОВ DATA MININGАннотация. 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.

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