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Open AccessArticle

Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends

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Centre for Fashion Business and Innovation Research, Fashion Business School, London College of Fashion, University of the Arts London, 272 High Holborn, London WC1V 7EY, UK
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Research Institute of Energy Management and Planning, University of Tehran, Tehran 1417466191, Iran
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Department of Business, Marketing and Law, School of Business, University of South-Eastern Norway, Bredalsveien 14, 3511 Hønefoss, Norway
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Centre for Fashion Business and Innovation Research, Fashion Business School, London College of Fashion, University of the Arts London, 272 High Holborn, London WC1V 7EY, UK
*
Author to whom correspondence should be addressed.
Soc. Sci. 2019, 8(4), 111; https://doi.org/10.3390/socsci8040111
Received: 22 February 2019 / Revised: 25 March 2019 / Accepted: 29 March 2019 / Published: 4 April 2019
(This article belongs to the Special Issue Fashion Merchandising and Consumer Behavior)
This paper aims to discuss the current state of Google Trends as a useful tool for fashion consumer analytics, show the importance of being able to forecast fashion consumer trends and then presents a univariate forecast evaluation of fashion consumer Google Trends to motivate more academic research in this subject area. Using Burberry—a British luxury fashion house—as an example, we compare several parametric and nonparametric forecasting techniques to determine the best univariate forecasting model for “Burberry” Google Trends. In addition, we also introduce singular spectrum analysis as a useful tool for denoising fashion consumer Google Trends and apply a recently developed hybrid neural network model to generate forecasts. Our initial results indicate that there is no single univariate model (out of ARIMA, exponential smoothing, TBATS, and neural network autoregression) that can provide the best forecast of fashion consumer Google Trends for Burberry across all horizons. In fact, we find neural network autoregression (NNAR) to be the worst contender. We then seek to improve the accuracy of NNAR forecasts for fashion consumer Google Trends via the introduction of singular spectrum analysis for noise reduction in fashion data. The hybrid neural network model (Denoised NNAR) succeeds in outperforming all competing models across all horizons, with a majority of statistically significant outcomes at providing the best forecast for Burberry’s highly seasonal fashion consumer Google Trends. In an era of big data, we show the usefulness of Google Trends, denoising and forecasting consumer behaviour for the fashion industry. View Full-Text
Keywords: Google Trends; fashion; forecast; neural networks; singular spectrum analysis; big data Google Trends; fashion; forecast; neural networks; singular spectrum analysis; big data
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Silva, E.S.; Hassani, H.; Madsen, D.Ø.; Gee, L. Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends. Soc. Sci. 2019, 8, 111.

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