Scholarly record
USE OF AUTOREGRESSIVE MODELS TO ESTIMATE A DEMAND FOR HARD COAL
Abstract
Forecasting a demand for hard coal is no easy feat, especially at times when there are constant transformations of the country?s energy mix structure. This stage should be realized in every mining company as grave mistakes in estimating a prospective sale may result in a disadvantageous economic situation of a company. The article presents an autoregressive model which mining companies may use to analyse the predicted sale of a raw material in order to plan an action strategy based on the achieved results.
Publication Impact Profile
Publication details
References5
Zeliaś A., Pawełek B., Wanat S., Prognozowanie ekonomiczne, teoria, przykłady, zadania, Wydawnictwo Naukowe PWN, Poland, ,2003, pp. 233
Sokołowski A., Analiza szeregów czasowych i prognozowanie, materiały StatSoft, 2010, pp. 81-102
Bielińska E., Identyfikacja procesów, Wydawnictwo Politechniki Śląskiej, Poland 1997, pp. 63
Paszcza H., Marzec R.: Górnictwo węgla kamiennego w Polsce w perspektywie roku 2015 – wybrane aspekty funkcjonowania sektora, Materiały konferencyjne Szkoły Eksploatacji Podziemnej 2008, Kraków 2008, s. 53
Paszcza H., Górnictwo węgla kamiennego w Polsce w roku 2017, Wiadomości Górnicze 5/2017
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.
