Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends
| Autor: | Liz Gee, Dag Øivind Madsen, Emmanuel Sirimal Silva, Hossein Hassani |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry 05 social sciences Exponential smoothing Big data forecast Univariate Fashion forecasting General Social Sciences Google Trends singular spectrum analysis neural networks Hybrid neural network lcsh:Social Sciences lcsh:H Analytics big data 0502 economics and business fashion Econometrics 050211 marketing Autoregressive integrated moving average business 050203 business & management Consumer behaviour |
| Zdroj: | Social Sciences Volume 8 Issue 4 Social Sciences, Vol 8, Iss 4, p 111 (2019) |
| ISSN: | 2076-0760 |
| DOI: | 10.3390/socsci8040111 |
| Popis: | 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&mdash a British luxury fashion house&mdash as an example, we compare several parametric and nonparametric forecasting techniques to determine the best univariate forecasting model for &ldquo Burberry&rdquo 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&rsquo 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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