Forecasting the consumption of plates in plants producing heavy plate cut shapes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F10%3A86076813" target="_blank" >RIV/61989100:27360/10:86076813 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Forecasting the consumption of plates in plants producing heavy plate cut shapes

Popis výsledku v původním jazyce

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 disruptedby 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.

Název v anglickém jazyce

Forecasting the consumption of plates in plants producing heavy plate cut shapes

Popis výsledku anglicky

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 disruptedby 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.

Druh

D - Stať ve sborníku

CEP obor

AH - Ekonomie

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2010

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

METAL 2010

ISBN

978-80-87294-17-8

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

—

Název nakladatele

TANGER s.r.o. Ostrava

Místo vydání

Rožnov pod Radhoštěm

Místo konání akce

Rožnov pod Radhoštěm

Datum konání akce

18. 5. 2010

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—