Abstract
Ordering proper amount of products, taking into account the demand of market, in fashion retail industries is one of the core challenges. Essentially due to the fact that the ordering is typically performed once in the each season, it is absolutely required to carry out precise orders. To make a precise ordering as well as to prevent overstocks and stock-out, there is a need for reliable forecasting methods. A reliable forecasting requires to consider proper predictive models which can consider all deciding factors. Specifically in the case of fashion forecasting since each product is associated with several factors, e.g. price, style, color and even human factors, learn a suitable predictive model is not an easy task. In fact, the challenge here boils down to learn a powerful model, which can cover all these information. To this end, big data techniques, namely data mining and machine learning methods serve the ability to accomplish the challenge. In this paper, we exploit unsupervised learning methods for a goal fitting the data, particularly w.r.t simple models although with higher gain. In essence, our innovative model is able to modify simple regression model, and hence, provide more promising results. In this regard, we apply big data analyses and techniques specifically in fashion field to analyze and make the salesprediction.
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References
Ahmad, A.; Dey, L. (2007): A k-mean clustering algorithm for mixed numeric and categorical data. Data & Knowledge Engineering, 63(2):503–527.
Bishop, C. M. (2006): Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Secaucus, NJ , USA.
Choi, T. M.; Hui, C. L.; Ng, S. F.; Yu, Y. (2012): Color trend forecasting of fashionable products with very few historical data, in: Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 42(6):1003–1010.
Choi, T. M.; Hui, C. L.; Yu, Y. (2013): Intelligent Fashion Forecasting Systems: Models and Applications. Springer Publishing Company.
Ehmke, J. F. (2012): Integration of Information and Optimization Models for Routing in City Logistics. Springer Publishing Company.
Frank, C.; Garg, A.; Sztandera, L.; Raheja, A. (2003): Forecasting women’s apparel sales using mathematical modeling. International Journal of Clothing Science and Technology, 15(2):107–125.
Frünkranz, J.; Hüllermeier, E. (2011): Preference learning. Künstliche Intelligenz, Springer.
Green, M.; Harrison, P. J. (1973): Fashion forecasting for a mail order company using a bayesian approach. Journal of the Operational Research Society, 24(2):193–205.
Gu, W.; Liu, X. (2010): Computer-assisted color database for trend forecasting. In Computational Intelligence and Software Engineering (CiSE), pages 1–4.
Happiette, M.; Rabenasolo, B.; Boussu, F. (1996): Sales partition for forecasting into textile distribution network. In Systems, Man, and Cybernetics, 1996., IEEE International Conference on, volume 4, pages 2868–2873 vol.4.
Hastie, T.; Tibshirani, R.; Friedman, J. (2001): The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
Liu, N.; Ren, S.; Choi, T. M., Hui, C. L.; Ng S. F. (2013): Sales Forecasting for Fashion Retailing Service Industry: A Review, Mathematical Problems in Engineering, vol. 2013, Article ID 738675, 9 pages, 2013. doi:10.1155/2013/738675
Mostard, J.; Teunter, R.; Koster, R. (2011): Forecasting demand for single-period products: A case study in the apparel industry. European Journal of Operational Research, 211(1):139–147.
Nenni, M. E.; Giustiniano, L.; Pirolo, L. (2013): Demand forecasting in the fashion industry: a review, International Journal of Engineering Business Management.
Schölkopf, B.; Smola, A.; Müller, K. R. (1999): Kernel principal component analysis. In Advances in kernel methods: Support vector learning, pages 327–352. MIT Press.
Sun, Z. L.; Choi, T. M.; Au, K. F.; Yu. Y. (2008): Sales forecasting using extreme learning machine with applications in fashion retailing. Decision Support Systems, 46(1):411–419.
Thomassey, S.; Castelain, J. M. (2002): An automatic textile sales forecast using fuzzy treatment of explanatory variables. Journal of Textile and Apparel, Technology and Management.
Thomassey, S.; Happiette, M.; Castelain, J. M. (2002): A short term forecasting system adapted to textile distribution. pages 1889–1893. IPMU 2002, Annecy, France.
Thomassey, S.; Happiette, M.; Castelain J. M. (2002): Textile items classification for sales forecasting. Proceeding 14th European Simulation Symposium (ESS).
Thomassey, S.; Happiette, M. (2007): A neural clustering and classification system for sales forecasting of new apparel items. Applied Soft Computing, 7(4):1177–1187, 2007. Soft Computing for Time Series Prediction.
Tsoumakas G.; Katakis I. (2007): Multi-label classification: An overview. Int J Data Warehousing and Mining, 2007:1–13.
Vroman, P.; Happiette, M.; Rabenasolo, B. (1998): Fuzzy adaptation of the holt-winter model for textile sales-forecasting. Journal of the Textile Institute, 89(1):78–89.
Xia, M.; Zhang, Y.; Weng, L.; Ye, X. (2012): Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs. Knowledge-Based Systems, 36(0):253–259.
Yelland, P. M.; Dong X. (2014): Forecasting demand for fashion goods: a hierarchical bayesian approach. In Tsan-Ming Choi, Chi-Leung Hui, and Yong Yu, editors, Intelligent Fashion Forecasting Systems: Models and Applications, pages 71–94. Springer, Berlin-Heidelberg.
Yesil, E.; Kaya, M.; Siradag, S. (2012): Fuzzy forecast combiner design for fast fashion demand forecasting. In Innovations in Intelligent Systems and Applications (INISTA), 2012 International Symposium on, pages 1–5.
Yu, E.; Choi, T. M.; Hui C. L. (2011): An intelligent fast sales forecasting model for fashion products. Expert Systems with Applications, 38(6):7373–7379.
Zhu, J.; Hastie, T. (2001): Kernel logistic regression and the import vector machine. In Journal of Computational and Graphical Statistics, pages 1081–1088. MIT Press.
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Tehrani, A., Ahrens, D. (2016). Improved Forecasting and Purchasing of Fashion Products based on the Use of Big Data Techniques. In: Bogaschewsky, R., Eßig, M., Lasch, R., Stölzle, W. (eds) Supply Management Research. Advanced Studies in Supply Management. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-08809-5_13
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DOI: https://doi.org/10.1007/978-3-658-08809-5_13
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