AI-Based Fashion Sales Forecasting Methods in Big Data Era

  • Shuyun Ren
  • Chi-leung Patrick Hui
  • Tsun-ming Jason Choi
Part of the Springer Series in Fashion Business book series (SSFB)


The demand of fashionable products is much difficult to be forecasted owing to the short-life cycle and high volatility driven by the ever-changing fashion trend. Many artificial intelligent (AI) methods and AI-based hybrid methods have been proven to be efficient for conducting fashion sales forecasting in previous studies. With the development and application of big data, information analytics would definitely lead to benefit for fashion sales forecasting, operation management, even the whole fashion supply chain coordination. However, few researches have studied the applicability of AI methods with big data. As we know, AI-based forecasting methods are time consuming and complex processing. In this chapter, we determine whether they are suitable and efficient for conducting fashion sales forecasting by high dimensional and large data. This paper aims to provide an up-to-date review on the commonly used and more efficient AI-based fashion sales forecasting methods and further examines the applicability of these methods in big data. How to make better use of these methods in big data era will also be conducted.


Artificial intelligent (AI) Big data Fashion industry Sales forecasting 


  1. Atsalakis GS, Valavanis KP (2009) Forecasting stock market short-term trends using a neuro-fuzzy based methodology. Expert Syst Appl 36(7):10696–10707CrossRefGoogle Scholar
  2. Au K-F, Choi T-M, Yu Y (2008) Fashion retail forecasting by evolutionary neural networks. Int J Prod Econ 114(2):615–630CrossRefGoogle Scholar
  3. Azar AT (2010) Adaptive neuro-fuzzy systems. In: Fuzzy systems. In-Tech, Austria, 85–110Google Scholar
  4. Azar AT, Hassanien AE (2015) Dimensionality reduction of medical big data using neural-fuzzy classifier. Soft Comput 19(4):1115–1127CrossRefGoogle Scholar
  5. BenoíT FN, Van Heeswijk M, Miche Y et al (2013) Feature selection for nonlinear models with extreme learning machines. Neurocomputing 102:111–124CrossRefGoogle Scholar
  6. Carbonneau R, Laframboise K, Vahidov R (2008) Application of machine learning techniques for supply chain demand forecasting. Eur J Oper Res 184(3):1140–1154CrossRefGoogle Scholar
  7. Cauwenberghs G, Poggio T (2000) Incremental and decremental support vector machine learning. In: Proceedings advances in neural information processing systems. Vancouver, CanadaGoogle Scholar
  8. Chen S-M, Chen C-D (2011) TAIEX forecasting based on fuzzy time series and fuzzy variation groups. IEEE Trans Fuzzy Syst 19(1):1–12CrossRefGoogle Scholar
  9. Chen T, Wang MJJ (1999) Forecasting methods using fuzzy concepts. Fuzzy Sets Syst 105(3):339–352CrossRefGoogle Scholar
  10. Chen J, Chen H, Wan X et al (2016) MR-ELM: a MapReduce-based framework for large-scale ELM training in big data era. Neural Comput Appl 27(1):101–110CrossRefGoogle Scholar
  11. Choi T-M, Sethi S (2010) Innovative quick responseprograms: a review. Int J Prod Econ 127(1):1–12CrossRefGoogle Scholar
  12. Choi TM, Hui CL, Yu Y (eds) (2013) Intelligent fashion forecasting systems: models and applications. Springer, New York, NY, USAGoogle Scholar
  13. Choi TM, Chan HK, Yue X (2016) Recent development in big data analytics for business operations and risk managementGoogle Scholar
  14. Derakhshan R, Orlowska ME Li X (2007) RFID data management: challenges and opportunities. In: IEEE International conference on RFID March 2007, vol 10Google Scholar
  15. Dittrich J, Quiané-Ruiz JA (2012) Efficient big data processing in Hadoop MapReduce. Proc VLDB Endow 5(12):2014–2015CrossRefGoogle Scholar
  16. El-Bakry HM, Mastorakis N (2008) A new fast forecasting technique using high speed neural networks. WSEAS Trans Signal Process 4(10):573–595Google Scholar
  17. Escoda I, Ortega A, Sanz A, Herms A (1997) Demand forecast by neuro-fuzzy techniques. In: Proceedings of the 6th IEEE international conference on fussy systems (FUZZ-IEEE’97), July 1997. pp 1381–1386Google Scholar
  18. Frank C, Garg A, Sztandera L, Raheja A (2003) Forecasting women’s apparel sales using mathematical modeling. Int J Cloth Sci Technol 15(2):107–125CrossRefGoogle Scholar
  19. Han D, Chan L, Zhu N (2007) Flood forecasting using support vector machines. J Hydroinform 9(4):267–276CrossRefGoogle Scholar
  20. Hsu LC, Wang CH (2007) Forecasting the output of integrated circuit industry using a grey model improved by the Bayesian analysis. Technol Forecast Soc Chang 74(6):843–853CrossRefGoogle Scholar
  21. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501CrossRefGoogle Scholar
  22. Hui C-L, Lau T-W, Ng S-F, Chan C-C (2005) Learningbased fuzzy colour prediction system for more effective apparel design. Int J Cloth Sci Technol 17(5):335–348CrossRefGoogle Scholar
  23. Jandaghi G, Tehrani R, Hosseinpour D, Gholipour R, Shadkam SAS (2010) Application of fuzzy-neural networks in multi-ahead forecast of stock price. Afr J Bus Manag 4(6):903–914Google Scholar
  24. Kok AG, Shang KH (2007) Inspection and replenishment policies for systems with inventory record inaccuracy. Manuf Serv Oper Manag 9(2):185–205CrossRefGoogle Scholar
  25. Koturwar P, Girase S, Mukhopadhyay D (2015) A survey of classification techniques in the area of big data. arXiv:1503.07477
  26. Lee HL, Padmanabhan V, Whang S (1997) The bullwhip effect in supply chain. Sloan Manag Rev 38(3):93–102Google Scholar
  27. Lin HT, Lin CJ (2003) A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. Neural Comput 1–32Google Scholar
  28. Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491–502CrossRefGoogle Scholar
  29. Liu N, Ren S, Choi T-M, Hui C.-L, Ng S-F (2013) Sales forecasting for fashion retailing service industry: a review. Math Probl Eng.
  30. Man ZH, Lee K, Wang DH, Cao ZW, Miao CY (2011) A new robust training algorithm for a class of single-hidden layer feedforward neural networks. Neurocomputing 74(16):2491–2501CrossRefGoogle Scholar
  31. Man ZH, Lee K, Wang DH, Cao ZW, Khoo SY (2012) Robust single-hidden layer feedforward network-based pattern classifier. IEEE Trans Neural Netw Learn Syst 23(12):1974–1986CrossRefGoogle Scholar
  32. Mastorocostas PA, Theocharis JB, Petridis VS (2001) A constrained orthogonal least-squares method for generating TSK fuzzy models: application to short-term load forecasting. Fuzzy Sets Syst 118(2):215–233CrossRefGoogle Scholar
  33. Miorandi D, Sicari S, De Pellegrini F, Chlamtac I (2012) Internet of things: vision, applications and research challenges. Ad Hoc Netw 10(7):1497–1516CrossRefGoogle Scholar
  34. Mirza B, Lin Z, Toh KA (2013) Weighted online sequential extreme learning machine for class imbalance learning. Neural Process Lett 38(3):465–486CrossRefGoogle Scholar
  35. Nenni ME, Giustiniano L, Pirolo L (2013) Demand forecasting in the fashion industry: a review. Int J Eng Bus ManagCrossRefGoogle Scholar
  36. Ngai EWT, Moon KKL, Riggins FJ, Yi CY (2008) RFID research: an academic literature review (1995–2005) and future research directions. Int J Prod Econ 112(2) 510–520CrossRefGoogle Scholar
  37. Ren S, Choi T-M, Liu N (2015) Fashion sales forecasting with a panel data-based particle-filter model. IEEE Trans Syst Man Cybern Syst 45(3), 411–421CrossRefGoogle Scholar
  38. Rong HJ, Ong YS, Tan AH, Zhu Z (2008) A fast pruned-extreme learning machine for classification problem. Neurocomputing 72(1–3):359–366CrossRefGoogle Scholar
  39. Ruping S, Morik K (2003) Support vector machines and learning about time. Paper presented at the Proceedings of ICASSP 2003Google Scholar
  40. Sagiroglu S, Sinanc D (2013) Big data: a review. In: 2013 international conference on collaboration technologies and systems (CTS). IEEE, pp 42–47Google Scholar
  41. Sarac A, Absi N, Dauzre-Prs S (2010) A literature review on the impact of RFID technologies on supply chain management. Int J Prod Econ 128(1):77–95CrossRefGoogle Scholar
  42. Saygin C, Sarangapani J, Grasman SE (2007) A systems approach to viable RFID implementation in the supply chain. In: Springer series in advanced manufacturing: trends in supply chain design and management technologies and methodologies vol 327Google Scholar
  43. Sun ZL, Au KF, Choi TM (2007) A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines. IEEE Trans Syst Man Cybern Part B Cybern 37(5):1321–1331CrossRefGoogle Scholar
  44. Sun ZL, Choi TM, Au KF, Yu Y (2008) Sales forecasting using extreme learning machine with applications in fashion retailing. Decis Support Syst 46(1):411–419CrossRefGoogle Scholar
  45. Swief RA, Hegazy YG, Abdel-Salam, TS Bader MA (2009) Support vector machines (SVM) based short term electricity load-price forecasting. In: 2009 IEEE bucharest powertech. pp 1–5Google Scholar
  46. Syed N, Liu H, Sung K (1999) Incremental learning with support vector machines. In: Proceedings the workshop on support vector machines at the international joint conference on artificial intelligence. Stockholm, SwedenGoogle Scholar
  47. Sztandera LM, Frank C, Vemulapali B (2004) Predicting women’s apparel sales by sot computing. In: Proceedings of the 7th international conference on artificial intelligence and sot computing (ICAISC’04), June 2004. Zakopane, Poland, pp 1193–1198Google Scholar
  48. Valizadegan H, Jin R (2006) Generalized maximum margin clustering and unsupervised kernel learning. In: Advances in neural information processing systems. pp 1417–1424Google Scholar
  49. Visich JK, Li S, Khumawala BM, Reyes PM (2009) Empirical evidence of RFID impacts on supply chain performance. Int J Oper Prod Manag 29(12):1290–1315CrossRefGoogle Scholar
  50. Vroman P, Happiette M, Rabenasolo B (1998) Fuzzy adaptation of the Holt-Winter model for textile sales-forecasting. J Text Inst 89(1):78–89CrossRefGoogle Scholar
  51. Wan C, Xu Z, Pinson P et al (2014) Probabilistic forecasting of wind power generation using extreme learning machine. IEEE Trans Power Syst 29(3):1033–1044CrossRefGoogle Scholar
  52. Wang D, Liu X, Wang M (2013) A DT-SVM strategy for stock futures prediction with big data. In: 2013 IEEE 16th International Conference on computational science and engineering (CSE). IEEE, pp 1005–1012Google Scholar
  53. Wu Q (2009) The forecasting model based on wavelet ν-support vector machine. Expert Syst Appl 36(4):7604–7610CrossRefGoogle Scholar
  54. Wu X, Zhu X, Wu GQ et al (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107CrossRefGoogle Scholar
  55. Xia M, Zhang Y, Weng L, Ye X (2012) Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs. Knowl-Based Syst 36:253–259CrossRefGoogle Scholar
  56. Xu Y, Dai Y, Dong ZY et al (2013) Extreme learning machine-based predictor for real-time frequency stability assessment of electric power systems. Neural Comput Appl 22(3–4):501–508CrossRefGoogle Scholar
  57. Yesil E, Kaya M, Siradag S (2012) Fuzzy forecast combiner design for fast fashion demand forecasting. In: Proceedings of the IEEE international symposium in innovations in intelligent systems and applications (INISTA’12). pp 1–5Google Scholar
  58. Yu H, Yang J, Han J (2003) Classifying large data sets using SVMs with hierarchical clusters. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 306–315Google Scholar
  59. Yu J, de Jong R, Lee L-F (2012) Estimation for spatial dynamic panel data with fixed effects: the case of spatial cointegration. J Econom 167(1):16–37CrossRefGoogle Scholar
  60. Zadeh LA (1965) Fuzzy sets. Inf Comput 8:338–353Google Scholar
  61. Zelbst PJ, Green KW Jr, Sower VE, Baker G (2010) RFID utilization and information sharing: the impact on supply chain performance. J Bus Indus Market 25(8):582–589CrossRefGoogle Scholar
  62. Zhu Q-Y, Qin AK, Suganthan PN, Huang G-B (2005) Evolutionary extreme learning machine. Pattern Recogn 38(10):1759–1763CrossRefGoogle Scholar
  63. Zimek A, Schubert E, Kriegel HP (2012) A survey on unsupervised outlier detection in high-dimensional numerical data. Stat Anal Data Min 5(5):363–387CrossRefGoogle Scholar
  64. Zong W, Huang GB, Chen Y (2013) Weighted extreme learning machine for imbalance learning. Neurocomputing 101:229–242CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Shuyun Ren
    • 1
  • Chi-leung Patrick Hui
    • 2
  • Tsun-ming Jason Choi
    • 2
  1. 1.Guangdong University of TechnologyGuangzhouChina
  2. 2.Institute of Textiles and ClothingThe Hong Kong Polytechnic UniversityHunghom, KowloonHong Kong

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