Title :
The Forecasting Models for Spare Parts Based on ARMA
Author :
Jiafu, Ren ; Zongfang, Zhou ; Fang, Zhang
Author_Institution :
Sch. of Manage. & Econ., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
fDate :
March 31 2009-April 2 2009
Abstract :
According to the historical data of timestimes Factory, we use ARIMA time series to model how to predict the demand for spare parts of timestimes Factory. The forecast model test results show that the model can better predict, with high accuracy. On this basis, this article predicts the demand for spare parts of next year.
Keywords :
autoregressive moving average processes; demand forecasting; maintenance engineering; time series; ARMA time series; spare part demand forecasting model; timestimes factory; Computer science; Decision making; Demand forecasting; Economic forecasting; Mathematical model; Power generation; Predictive models; Production facilities; Time series analysis; White noise; ARIMA; Demand forecast; Time Series;
Conference_Titel :
Computer Science and Information Engineering, 2009 WRI World Congress on
Conference_Location :
Los Angeles, CA
Print_ISBN :
978-0-7695-3507-4
DOI :
10.1109/CSIE.2009.315
