Abstract
The colour of a fashion item is one of its key features which often play an important role on the purchase decisions of consumers. And the fashionable colours often prevail in one season, thus, it is crucial for the fashion industry to do forecasting of the fashion trends, especially on colours, prior to the beginning the production for the target season. The lead-time of forecasting becomes shorter recent years with the intensified competition of global fashion industry, and imposes pressure on the forecasting of fashion colour trends. The common practise for the forecasting of colour trends in the fashion industry are based on the ideals of field experts, and the forecasting is in nature fuzzy and hard to be substituted by analytical models. In this paper, we explore the forecasting of colour trends by artificial intelligence models, especially artificial neural network and fuzzy logic models; we observed that such models help to improve the forecasting of fashion colour trends.
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References
Au K-F, Choi T-M, Yu Y (2008) Fashion retail forecasting by evolutionary neural networks. Int J Prod Econ 114(2):615–630
Cassidy TD, Cassidy T (2012) Using soft systems methodology to improve the colour forecasting process. J Int Colour Assoc 7:27–50
Cassidy G, Kamlet MS, Nagin DS (1989) An empirical examination of bias in revenue forecasts by state governments. Int J Forecast 5(3):321–331
Cassidy TD (2007) Personal colour analysis, consumer colour preferences and colour forecasting for the fashion and textile industries. Colour Des Creativity 1(1):1–14
Chang L-X, Gao W-D, Zhang X (2009) Discussion on fashion color forecasting for textile and fashion industries. J Fiber Bioeng Inform 2(1):15–21
Choi T-M, Yu Y, Au K-F (2011) A hybrid SARIMA wavelet transform method for sales forecasting. Decis Support Syst 51(1):130–140
Fashion-era (2012) http://www.fashion-era.com/
Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685
Lin JJ, Sun PT, Chen JJ-R, Wang LJ, Kuo HC, Kuo WG (2010) Applying gray model to predicting trend of textile fashion colors. J Text Inst 101(4):360–368
PANTONE (2012) http://www.pantone.com
Sun Z-L, Choi T-M, Au K-F, Yu Y (2008) Sales forecasting using extreme learning machine with applications in fashion retailing. Decis Support Syst 46(1):411–419
Wong WK, Guo ZX (2010) A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm. Int J Prod Econ 128(2):614–624
Yu Y, Choi T-M, Au K-F, Sun ZL (2010) Applications of evolutionary neural networks for sales forecasting of fashionable products. In: Olivas ES, Guerrero JDM, Martinez-Sober M et al (eds) Handbook of research on machine learning applications and trends: algorithms, methods, and techniques. IGI Global, pp 387–403
Yu Y, Hui C-L, Choi T-M (2010) Intelligent fabric hand prediction system with fuzzy neural network. IEEE Trans Syst Man Cybern C Appl Rev 40(6):619–629
Yu Y, Choi T-M, Hui C-L (2011) An intelligent fast sales forecasting model for fashion products. Expert Syst Appl 38(6):7373–7379
Yu Y, Hui C-L, Choi T-M (2012) An empirical study of intelligent expert systems on forecasting of fashion color trend. Expert Syst Appl 39(4):4383–4389
Zadeh LA (1965) Fuzzy sets. Inform Control 8(3):338–353
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Yu, Y., Ng, SF., Hui, CL., Liu, N., Choi, TM. (2014). Intelligent Fashion Colour Trend Forecasting Schemes: A Comparative Study. In: Choi, TM., Hui, CL., Yu, Y. (eds) Intelligent Fashion Forecasting Systems: Models and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39869-8_8
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DOI: https://doi.org/10.1007/978-3-642-39869-8_8
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