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Wyszukujesz frazę "model ARIMA" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Prediction of pork meat prices by selected methods as an element supporting the decision-making process
Autorzy:
Zielińska-Sitkiewicz, Monika
Chrzanowska, Mariola
Powiązania:
https://bibliotekanauki.pl/articles/2100136.pdf
Data publikacji:
2021
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
agricultural sector
pork price
forecast
creeping trend
ARIMA model
Opis:
Forecasts of economic processes can be determined using various methods, and each of them has its own characteristics and is based on specific assumptions. In the case of agriculture, forecasting is an essential element of efficient management of the entire farming process. The pork sector is one of the main agricultural sectors in the world. Pork consumption and supply are the highest among all types of meat, and Poland belongs to the group of large producers. The article analyses the price formation of class E pork, expressed in € per 100 kg of carcass, recorded from May 2004 to December 2019. The data comes from the Agri-food data portal. A creeping trend model with segments of linear trends of various lengths and the methodology of building ARIMA models are used to forecast these prices. The accuracy of forecasts is verified by forecasting ex post and ex ante errors, graphical analysis, and backcasting analysis. The study shows that both methods can be used in the prediction of pork prices.
Źródło:
Operations Research and Decisions; 2021, 31, 3; 137--152
2081-8858
2391-6060
Pojawia się w:
Operations Research and Decisions
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of statistical process control for proper processing of the Fore-Sudetic Monocline copper ore
Autorzy:
Tasdemir, A.
Kowalczuk, P. B.
Powiązania:
https://bibliotekanauki.pl/articles/110236.pdf
Data publikacji:
2014
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
copper
upgrading
statistical process control
ARIMA model
Shewhart’s chart
autocorrelation
Opis:
The paper deals with Statistical Process Control (SPC) applied to three original and three generated variables of copper ore upgrading by flotation. The six variables were evaluated by the SPC charts based on industrial upgrading of copper ore data gathered during one month of operation in the form of copper content in feed, concentrate and tailing. The remaining three upgrading variables were concentrate yield, copper recovery in concentrate and non-copper components recovery in tailing. Although, all variables obeyed normal distribution, considerable autocorrelation was detected between observations for all variables. For this reason, the traditional Shewhart control charts, that assume the process data generated are normally and independently distributed, resulted in many of out-of-control points which may lead to wrong decisions regarding the control of process variables. The most suitable ARIMA time series models were determined for all variables to remove autocorrelations. The ARIMA(0,1,1) model was found the best for copper content in feed, copper content in concentrate, concentrate yield and non-copper components recovery in tailing, while the AR(1) model was suitable for copper content in tailing and copper recovery in concentrate.
Źródło:
Physicochemical Problems of Mineral Processing; 2014, 50, 1; 249-264
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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