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Development of information components characterizing parameters of forecasting

Development of information components characterizing parameters of forecasting

Abstract

Для решения задач прогнозирования необходимо строить модели прогнозирования так, чтобы максимально адаптировать их не только к входным данным, или статистических данных, используемых для построения моделей прогноза, а и к условиям и особенностям тех процессов, в рамках которых происходят события, которые предполагается прогнозировать. Для этого, необходимо проводить анализ соответствующих процессов, принимая во внимание все элементы, которые могут влиять на возникновение события. Это позволяет выявить особенности события, которое предполагается прогнозировать и установить взаимосвязи между математическими переменными, используемых в модели и их интерпретационными описаниями.

For solving the problem of forecasting the need to build forecasting models so as to best adapt them not only to input data or statistics that are used to build prediction models, but also to the conditions and characteristics of the processes in which events occur that are intended to predict. For this purpose, it is necessary to analyze the relevant processes, taking into account all the elements that can influence the occurrence of the event. This allows you to identify the characteristics of the event, which is expected to forecast and to establish relationships between mathematical variables used in the model and its interpretative descriptions.

Для розв’язку задач прогнозування необхідно будувати моделі прогнозування так, щоб максимально адаптувати їх не тільки до вхідних даних, або статистичних даних, що використовуються для побудови моделей прогнозу, а і до умов та особливостей тих процесів, в рамках яких відбуваються події, які передбачається прогнозувати. Для цього, необхідно проводити аналіз відповідних процесів, приймаючи до уваги всі елементи, які можуть впливати на виникнення події. Це дозволяє виявити особливості події, яку передбачається прогнозувати та встановити взаємозв’язки між математичними змінними, що використовуються в моделі та їх інтерпретаційними описами.

Keywords

інформаційні компоненти, прогнозирование, information components, интерпретационное описание, прогнозування, interpretative description, forecasting, інтерпретаційний опис, информационные компоненты

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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold