Forecasting Demand for Fashion Goods:A Hierarchical Bayesian Approach

Phillip M. Yelland () and Xiaojing Dong ()
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Phillip M. Yelland: Google Inc.
Xiaojing Dong: Santa Clara University

Chapter Chapter 5 in Intelligent Fashion Forecasting Systems: Models and Applications, 2014, pp 71-94 from Springer

Abstract: Abstract A central feature of demand for products in the fashion apparel segment is a pronounced product life cycle—demand for a fashion product tends to rise and fall dramatically in accordance with the rate of public of adoption. Product demands that vary in such a manner can be difficult to forecast, especially in the critical early period of a product’s life, when observed demand can be a very unreliable yardstick of demand later on. This paper examines the applicability of a Bayesian forecasting model—based on one developed for use in the computer industry—to fashion products. To do so, we use an agent-based simulation to produce a collection of demand series consistent with commonly-accepted characteristics of fashion adoption. Using Markov chain Monte Carlo techniques to make predictions using the Bayesian model, we are able quantitatively to demonstrate its superior performance in this application.

Keywords: Forecast Model; Mean Absolute Percentage Error; Product Life Cycle; Product Demand; Posterior Predictive Distribution (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-39869-8_5

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DOI: 10.1007/978-3-642-39869-8_5

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