Introduction: Intelligent fashion forecasting

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

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Abstract

Forecasting is a classic topic of information systems [1] and it is a crucial part for companies in the fashion apparel industry. Despite the fact that there is no “perfect” forecast, forecasting for highly structured data (e.g., the time series with high seasonality or trend) is known to be “easy” because there are many well-established models which provide the needed analytical formulations [13, 18]. For example, Hott [1] develop analytical models with closed-form expressions for forecasting time series with prominent features of seasonality and trend by using the exponentially weighted moving average method. In addition, more sophisticated statistical methods such as SARIMA [2] and ARIMA have also been widely applied for these structured forecasting problems with good performance. However, for many real life applications in the fashion industry, the data patterns are notorious for being highly volatile and it is very difficult, if not impossible, to analytically learn about the underlining pattern and hence the well-established and traditional statistical methods will fail to make a sound prediction. As a result, recent advances of artificial intelligence (AI) technologies have provided the alternative way of providing precise and more accurate forecasting result for fashion sales time series. For example, Au et~al. [4] explore the fashion sales forecasting problem for fashion retailers by using evolutionary neural networks (ENN). They find that ENN can substantially enhance the forecasting accuracy compared to various other traditional methods. Although AI methods such as ENN can produce highly accurate forecasting results for volatile data sets, they suffer a major drawback in which they are slow (e.g., ENN can take hours in order to generate the forecasting results). This shortcoming becomes a major barricade which hinders the application of AI methods for forecasting in real world. Recently, in the literature, there are some innovative proposals and studies from different perspectives for establishing intelligent efficient forecasting systems with a focus on speed. Many of these proposed systems and models are inspiring and can lead to many promising applications. For example, El-Bakry and Mastorakis [5] propose an innovative approach which speeds up the prediction stage. To be specific, their method improves the forecasting speed by applying cross correlation between the whole input data and the weights of neural networks in the frequency domain. El-Bakry and Mastorakis prove analytically that this proposal can speed up the whole forecasting process and they call the resulting neural network a high speed neural network (HSNN) and discuss its use in time series forecasting. In Choi et~al. [2], in order to enhance the accuracy and versatility of SARIMA in conducting fashion sales forecasting, a novel hybrid approach by wavelet transform is developed. To be specific, Choi et~al. propose a scheme in which the original fashion sales time series is decomposed into components by wavelet transform. By conducting forecasting at the component level, the respective prediction results are obtained. Finally, in order to get the time series forecast for the original sales data, the component-level forecasts are transformed back to the original time series forecast. This hybrid wavelet transform SARIMA method has been tested with real and artificial data sets. Its performance is compared to both the pure statistical methods as well as some traditional AI methods, and is found to be satisfactory. Most recently, inspired by the strengths and weaknesses of pure statistical method (PSM) and the extended extreme learning machines (EELM), Yu et~al. [6] develop a novel algorithm which combines the EELM and the pure statistical model (PSM) to conduct intelligent fast forecasting for fashion sales time series. Their method has employed a sophisticated scheme to determine the optimal parameters of the algorithm which can achieve the best possible (expected) accuracy with EELM and PSM within the given time limit constraint. In addition to the pure statistical method, there are other newly emerged models which are fast and can yield comparable forecasting accuracies. For instance, the Grey Model (GM) [25] is one of such models. The GM has been employed in the study of fashion trend forecasting, and very favorable results have been reported in [22]. Such models like GM are also suitable candidates for modeling the fashion forecasting problems. Similarly, research work as in [24] would also require an algorithm which intelligently chooses between the models to accomplish efficient forecasting tasks.

Original languageEnglish
Title of host publicationIntelligent Fashion Forecasting Systems
Subtitle of host publicationModels and Applications
PublisherSpringer Berlin Heidelberg
Pages3-8
Number of pages6
ISBN (Electronic)9783642398698
ISBN (Print)9783642398681
DOIs
Publication statusPublished - 1 Jan 2014

ASJC Scopus subject areas

  • Economics, Econometrics and Finance(all)
  • Business, Management and Accounting(all)
  • Computer Science(all)

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