Forecasting of retail sales data using differential evolution

Author

Majhi, Ritanjali ; Panda, Ganapati ; Majhi, Babita ; Panigrahi, S.K. ; Mishra, Manoj Ku

Author_Institution

Sch. of Manage., Nat. Inst. of Technol., Warangal, India

fYear

2009

fDate

9-11 Dec. 2009

Firstpage

1343

Lastpage

1348

Abstract

The paper aims to develop an efficient forecasting model using differential evolution (DE) based learning rule. The structure chosen is an adaptive linear combiner whose weights are trained using DE. The prediction performance of the resulting model is evaluated by feeding features of retail sales data for different months´ ahead prediction. These results are compared with those obtained by GA based approach. The comparison demonstrates improved prediction of sales data by the proposed DE method.

Keywords

FIR filters; forecasting theory; genetic algorithms; learning (artificial intelligence); sales management; GA based approach; adaptive linear combiner; differential evolution; genetic algorithm; learning rule; retail sales data forecasting; Artificial neural networks; Educational technology; Engineering management; Information technology; Load forecasting; Marketing and sales; Paper technology; Predictive models; Technology forecasting; Technology management; Sales forecasting; adaptive linear combiner; differntial evolution;

fLanguage

English

Publisher

ieee

Conference_Titel

Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on

Conference_Location

Coimbatore

Print_ISBN

978-1-4244-5053-4

Type

conf

DOI

10.1109/NABIC.2009.5393740

Filename

5393740