Forecasting Computer Products Sales by Integrating Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Chi-Jie Lu and Yuehjen E. Shao

Mathematical Problems in Engineering, 2012, vol. 2012, 1-15

Abstract:

A hybrid forecasting model that integrates ensemble empirical model decomposition (EEMD), and extreme learning machine (ELM) for computer products sales is proposed. The EEMD is a new piece of signal processing technology. It is based on the local characteristic time scales of a signal and could decompose the complicated signal into intrinsic mode functions (IMFs). The ELM is a novel learning algorithm for single-hidden-layer feedforward networks. In our proposed approach, the initial task is to apply the EEMD method to decompose the original sales data into a number of IMFs. The hidden useful information of the original data could be discovered in those IMFs. The IMFs are then integrated with the ELM method to develop an effective forecasting model for computer products sales. Experimental results from three real computer products sales data, including hard disk, display card, and notebook, showed that the proposed hybrid sales forecasting method outperforms the four comparative models and is an effective alternative for forecasting sales of computer products.

Date: 2012
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2012/831201.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2012/831201.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:831201

DOI: 10.1155/2012/831201

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().