A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm

By W. Wong, Z. X. Guo. 2010

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Performance of state space and ARIMA models for consumer retail sales forecasting (2015, background): “Wong and Guo [13] propose a hybrid intelligent model using extreme learning machine (ELM) and a harmony search algorithm to forecast medium-term sales in fashion retail supply chains.”
Trends in extreme learning machines: A review (2015, background): “…price forecasting (Chen, Dong, et al., 2012), Temperature prediction of molten steel (Tian & Mao, 2010), sales forecasting (Chen & Ou, 2011; Wong & Guo, 2010), drying system modeling (Balbay, Kaya, & Sahin, 2012), security assessment of wind power system (Xu, Dong, Xu, Meng, & Wong, 2012;…”
Retail forecasting: Research and practice (2019, background): “Wood and Reynolds (2013) discuss how the models are combined with context specific knowledge and the judgments of location analysts and analogous information to produce final recommendations.”
Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities (2020, background): “As the literature reveals, artificial neural networks (ANN) are widely applied for demand forecasting [82–85].”
A survey on applications of the harmony search algorithm (2013, unknown): “(2011), Xu et al. (2011), Tangpattanakul et al.”