Title :
Logistics amount forecasting based on combined ARIMA and ANN model
Author :
Jing, Zhang ; Jin-fu, Zhu
Author_Institution :
Coll. of Econ. & Manage., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China
Abstract :
The logistics amount of some enterprises has a dual characters of growth and seasonal fluctuation. Multiple seasonal ARIMA model has linear fitting ability and ANN has the ability of nonlinear relationship mapping. A combined forecasting model based on multiple seasonal ARIMA model and ANN model was proposed to overcome the defects of single model, and the prediction result shows that the combined forecasting model is superior to the single model in many performance aspects. Combined forecasting model offers a new effective method of logistics amount prediction.
Keywords :
autoregressive moving average processes; forecasting theory; logistics data processing; neural nets; ANN model; ARIMA; enterprises; logistics amount forecasting; nonlinear relationship mapping; Artificial neural networks; Biological system modeling; Demand forecasting; Economic forecasting; Educational institutions; Fluctuations; Intelligent systems; Logistics; Predictive models; Time series analysis;
Conference_Titel :
Grey Systems and Intelligent Services, 2009. GSIS 2009. IEEE International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4914-9
Electronic_ISBN :
978-1-4244-4916-3
DOI :
10.1109/GSIS.2009.5408245