An intelligent fast sales forecasting model for fashion products
Journal
Expert Systems with Applications
Journal Volume
38
Journal Issue
6
Pages
7373-7379
Date Issued
2011
Author(s)
Abstract
Sales forecasting is crucial in fashion business because of all the uncertainty associated with demand and supply. Many models for forecasting fashion products are proposed in the literature over the past few decades. With the emergence of artificial intelligence models, artificial neural networks (ANN) are widely used in forecasting. ANN models have been revealed to be more efficient and effective than many traditional statistical forecasting models. Despite the reported advantages, it is relatively more time-consuming for ANN to perform forecasting. In the fashion industry, sales forecasting is challenging because there are so many product varieties (i.e.; SKUs) and prompt forecasting result is needed. As a result, the existing ANN models would become inadequate. In this paper, a new model which employs both the extreme learning machine (ELM) and the traditional statistical methods is proposed. Experiments with real data sets are conducted. A comparison with other traditional methods has shown that this ELM fast forecasting (ELM-FF) model is quick and effective. ? 2010 Elsevier Ltd. All rights reserved.
Subjects
Artificial neural network; Extreme learning machine; Sales forecasting
Other Subjects
Artificial Neural Network; Demand and supply; Extreme learning machine; Fashion industry; New model; Product variety; Real data sets; Sales forecasting; Statistical forecasting; Learning systems; Neural networks; Sales; Forecasting
Type
journal article