Forecasting methods in modern enterprise management
Authors:
- Dariusz Grzesica
Abstract
The aim of the chapter is to describe advanced methods of forecasting used in modern, high-tech enterprises. One of them – the ARMA model takes into account the strong dependence between the individual observations, used for prediction of time series, characterized by high dynamics of change. Here will be explained the process of selection of its parameters, and method design of the model. Second – harmonic analysis – uses, in turn, the cyclicality of the time series on the basis of which to construct the model describing the time series and what is the forecast for future periods. The first method is a group of parametric models, the second one is nonparametric, and both use the nature of a change of time series. The use of these methods will be shown by example.
- Record ID
- CUT3c600ab31fe643b7bd9d0dafd01d194d
- Publication categories
- ; ;
- Author
- Pages
- 193-205
- Other elements of collation
- tab.; wykr.; Bibliografia (na s.) - 204-205; Oznaczenie streszczenia - Abstr.
- Book
- Nalepka Adam, Adam Nalepka Ujwary-Gil Anna Anna Ujwary-Gil (eds.): Business and non-profit organizations facing increased competition and growing customers’ demands : proceedings of the 16th International Conference: Tomaszowice, Poland, 19-20 June 2017, Business and Non-profit Organizations Facing Increased Competitions and Growing Customers' Demands, no. 16, 2017, Nowy Sącz, Nowy Targ, Wyższa Szkoła Biznesu – National-Louis University in Nowy Sącz, Foundation for the Dissemination of Knowledge and Science "Cognitione", ISBN 978-83-949144-0-0 (print ISBN)
- Keywords in English
- advanced forecasting methods, ARMA model, harmonic analysis
- URL
- http://konferencja.jemi.edu.pl/pl/monografie Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 20
- Additional fields
- Indeksowana w: Web of Science
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT3c600ab31fe643b7bd9d0dafd01d194d/
- URN
urn:pkr-prod:CUT3c600ab31fe643b7bd9d0dafd01d194d
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