U.S. Geological Survey, Metals and minerals: U.S. Geological Survey Minerals Yearbook, v. I. Online. Dostupné z: https://www.usgs.gov/centers/national-minerals-information-center/minerals-yearbook-metals-and-minerals
HYNDMAN, Rob J. a ATHANASOPOULOS, George. Forecasting: principles and practice. Second edition. [Melbourne]: OTexts, 2018. ISBN 978-0-9875071-1-2.
MONTGOMERY, Douglas C.; JENNINGS, Cheryl L. a KULAHCI, Murat. Introduction to time series analysis and forecasting. Second edition. Wiley series in probability and statistics. Hoboken, New Jersey: Wiley, 2015. ISBN 978-1-118-74511-3 .
TETLOCK, Philip a GARDNER, Dan. Superforecasting: the art and science of prediction. New York: BDWY, Brodway Books, [2015]. ISBN 978-0-8041-3671-6 .
Forecasting in the raw materials industry
| Language of instruction | čeština |
|---|---|
| Code | 545-0229 |
| Abbreviation | PSP |
| Course title | Forecasting in the raw materials industry |
| Coordinating department | Department of Economics and Control Systems |
| Course coordinator | RNDr. Radmila Sousedíková, Ph.D. |
Summary
The course provides students with theoretical knowledge and practical skills in the field of forecasting and application of forecasting methods in the raw materials industry, with a special focus on critical raw materials. Students will become familiar with the importance of forecasting for economic and managerial decision-making, basic forecasting concepts, classification of forecasts and principles of using forecasting methods in decision-making.
After completing the course, the student will achieve the following learning outcomes:
Knowledge: the student will explain the importance of forecasting for economic and strategic decision-making, characterize basic forecasting concepts and principles of the forecasting process, distinguish individual types of forecasts; characterize basic qualitative and quantitative forecasting methods, explain the principles of extrapolation methods, characterize the importance of global forecasts in the field of raw materials and explain the specifics of forecasting the development of production and consumption and prices of mineral commodities.
Skills: the student applies qualitative forecasting methods in assessing future development, creates a forecast of production and consumption of a selected mineral commodity, creates a forecast of the price of a selected mineral commodity, interprets the results of forecasts in the context of the development of the raw materials industry.
General competencies: the student independently selects an appropriate working procedure and a suitable forecasting method according to the nature of the problem being solved, critically evaluates the assumptions, uncertainties and risks associated with forecasting future developments; compares various forecasting scenarios and assesses their significance for decision-making, professionally interprets and presents the results of the forecasting analysis.
After completing the course, the student will achieve the following learning outcomes:
Knowledge: the student will explain the importance of forecasting for economic and strategic decision-making, characterize basic forecasting concepts and principles of the forecasting process, distinguish individual types of forecasts; characterize basic qualitative and quantitative forecasting methods, explain the principles of extrapolation methods, characterize the importance of global forecasts in the field of raw materials and explain the specifics of forecasting the development of production and consumption and prices of mineral commodities.
Skills: the student applies qualitative forecasting methods in assessing future development, creates a forecast of production and consumption of a selected mineral commodity, creates a forecast of the price of a selected mineral commodity, interprets the results of forecasts in the context of the development of the raw materials industry.
General competencies: the student independently selects an appropriate working procedure and a suitable forecasting method according to the nature of the problem being solved, critically evaluates the assumptions, uncertainties and risks associated with forecasting future developments; compares various forecasting scenarios and assesses their significance for decision-making, professionally interprets and presents the results of the forecasting analysis.
Literature
Doporučená literatura
CARRARA, Samuel; BOBBA, Silvia; BLAGOEVA, Darina; ALVES DIAS, Patricia and CAVALLI, Alessandro et al. Supply chain analysis and material demand forecast in strategic technologies and sectors in the EU – A foresight study. Online, PDF. Luxembourg: Publications Office of the European Union, 2023. Dostupné z: https://doi.org/10.2760/386650, JRC132889.
CARLBERG, Conrad George. Predictive analytics: Microsoft Excel. Second edition. Conrad Carlberg's Microsoft Excel analytics series. Indianapolis, Indiana: Que, 2018] ISBN 978-0-7897-5835-4.
GILLILAND, Michael; TASHMAN, Len a SGLAVO, Udo (ed.). Business forecasting: the emerging role of artificial intelligence and machine learning. Wiley and SAS business series. Hoboken, New Jersey: Wiley, 2021. ISBN 978-1-119-78247-6.
WOODWARD, Wayne A.; SADLER, Bivin Philip a ROBERTSON, Stephen D. Time series for data science: analysis and forecasting. Chapman & Hall/CRC texts in statistical science series. Boca Raton: CRC Press, Taylor & Francis Group, 2022. ISBN 978-0-367-54389-1.
CARLBERG, Conrad George. Predictive analytics: Microsoft Excel. Second edition. Conrad Carlberg's Microsoft Excel analytics series. Indianapolis, Indiana: Que, 2018] ISBN 978-0-7897-5835-4.
GILLILAND, Michael; TASHMAN, Len a SGLAVO, Udo (ed.). Business forecasting: the emerging role of artificial intelligence and machine learning. Wiley and SAS business series. Hoboken, New Jersey: Wiley, 2021. ISBN 978-1-119-78247-6.
WOODWARD, Wayne A.; SADLER, Bivin Philip a ROBERTSON, Stephen D. Time series for data science: analysis and forecasting. Chapman & Hall/CRC texts in statistical science series. Boca Raton: CRC Press, Taylor & Francis Group, 2022. ISBN 978-0-367-54389-1.