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Time-Constrained Fashion Sales Forecasting by Extended Random Vector Functional Link Model

Time-Constrained Fashion Sales Forecasting by Extended Random Vector Functional Link Model
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Author(s): Yong Yu (The Hong Kong Polytechnic University, Hong Kong), Tsan-Ming Choi (The Hong Kong Polytechnic University, Hong Kong) and Chi-Leung Hui (The Hong Kong Polytechnic University, Hong Kong)
Copyright: 2012
Pages: 7
Source title: Fashion Supply Chain Management: Industry and Business Analysis
Source Author(s)/Editor(s): Tsan-Ming Choi (The Hong Kong Polytechnic University, Hong Kong)
DOI: 10.4018/978-1-60960-756-2.ch010

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Abstract

Forecasting is about providing estimation of the future that cannot be observed at the moment. In this chapter, the random vector functional link (RVFL), which is a variation of the artificial neural networks (ANN) model, is used in establishing a fashion sales forecasting model. It is well-known that the RVFL inherits the learning and approximation capability of ANN, while running much faster than the traditional ANN. In order to develop a real world forecasting application, we propose a time-constrained forecasting model (TCFM), implemented by an extended RVFL, in which the user can define the time limit and a precision threshold for yielding the forecasting result. Real datasets collected from a fashion retail company are employed for the analysis. Our experiment has shown that the proposed TCFM can produce quality forecasting within the given time constraint. Future research directions are outlined.

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