Color trend forecasting of fashionable products with very few historical data

Tsan Ming Choi, Chi Leung Hui, Sau Fun Ng, Yong Yu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

50 Citations (Scopus)


In time-series forecasting, statistical methods and various newly emerged models, such as artificial neural network (ANN) and grey model (GM), are often used. No matter which forecasting method one would apply, it is always a huge challenge to make a sound forecasting decision under the condition of having very few historical data. Unfortunately, in fashion color trend forecasting, the availability of data is always very limited owing to the short selling season and life of products. This motivates us to examine different forecasting models for their performances in predicting color trend of fashionable product under the condition of having very few data. By employing real sales data from a fashion company, we examine various forecasting models, namely ANN, GM, Markov regime switching, and GMANN hybrid models, in the domain of color trend forecasting with a limited amount of historical data. Comparisons are made among these models. Insights on the appropriate choice of forecasting models are generated.
Original languageEnglish
Article number6118333
Pages (from-to)1003-1010
Number of pages8
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Issue number6
Publication statusPublished - 4 Jan 2012


  • Artificial neural network (ANN)
  • fashion color trend forecasting
  • grey model (GM)
  • intelligent systems
  • Markov regime switching (MS) grey

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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