TY - GEN
T1 - Use of autoregressive models to estimate a demand for hard coal
AU - Manowska, Anna
N1 - Publisher Copyright:
© SGEM2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Demand for hard coal
KW - Forecasting
UR - https://www.scopus.com/pages/publications/85058888159
U2 - 10.5593/sgem2018/5.3/S28.124
DO - 10.5593/sgem2018/5.3/S28.124
M3 - Conference contribution
AN - SCOPUS:85058888159
SN - 9786197408355
T3 - International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM
SP - 975
EP - 982
BT - Micro and Nano Technologies, Space Technologies and Planetary Science
PB - International Multidisciplinary Scientific Geoconference
T2 - 18th International Multidisciplinary Scientific Geoconference, SGEM 2018
Y2 - 2 July 2018 through 8 July 2018
ER -