. . . "27360" . . "Forecasting the consumption of plates in plants producing heavy plate cut shapes"@en . "Ro\u017Enov pod Radho\u0161t\u011Bm" . "Ro\u017Enov pod Radho\u0161t\u011Bm" . . "978-80-87294-17-8" . . . "METAL 2010" . . . "Forecasting the consumption of plates in plants producing heavy plate cut shapes"@en . "RIV/61989100:27360/10:86076813!RIV11-MSM-27360___" . . "Lenort, Radim" . . "Sta\u0161, David" . "TANGER s.r.o. Ostrava" . "RIV/61989100:27360/10:86076813" . . "Feliks, J." . . . . . . "The paper is focused on search for suitable prediction models used for medium-term forecasting of the consumption of plates in plants producing heavy plate cut shapes. Demand time series for five product families, from the point of view of steel grade, have been assorted for this purpose. The time series include monthly demand data for the period from January 2007 to December 2009. Firstly, quantitative techniques based on time series analysis were used for the forecasting: simple moving average model with a multiplicative seasonal adjustment, Winter's exponential smoothing model and seasonal autoregressive integrated moving average (SARIMA) model. However, the application of these models is connected with two problems. First, time series are disrupted by the world crisis impacts. Second, time series does not affect the cycle component. That is why a prediction model using multilayer artificial neural network has been created."@en . "The paper is focused on search for suitable prediction models used for medium-term forecasting of the consumption of plates in plants producing heavy plate cut shapes. Demand time series for five product families, from the point of view of steel grade, have been assorted for this purpose. The time series include monthly demand data for the period from January 2007 to December 2009. Firstly, quantitative techniques based on time series analysis were used for the forecasting: simple moving average model with a multiplicative seasonal adjustment, Winter's exponential smoothing model and seasonal autoregressive integrated moving average (SARIMA) model. However, the application of these models is connected with two problems. First, time series are disrupted by the world crisis impacts. Second, time series does not affect the cycle component. That is why a prediction model using multilayer artificial neural network has been created." . "5"^^ . "shapes; cut; plate; heavy; producing; plants; plates; consumption; Forecasting"@en . . . "259511" . . "S" . . "Forecasting the consumption of plates in plants producing heavy plate cut shapes" . "1"^^ . "3"^^ . "[FA7CC8CF9939]" . "Forecasting the consumption of plates in plants producing heavy plate cut shapes" . "2010-05-18+02:00"^^ .