Fashion sales forecasting

Key words fashion sales forecasting, harmony search, neural network, extreme learning machine. 

To provide a flexible, robust and effective methodology for fashion sales forecasting, this chapter examines the sales forecasting problem based on the forecasting process in real-world fashion retailing, which forecasts the total sales amount of each fashion item category or each city (all categories) on a medium-term basis (annually, quarterly and monthly). 

Intelligent sales forecasting for fashion retailing using harmony search algorithms and extreme learning machines... 

To evaluate the performanee of the proposed forecasting model, extensive experiments were conducted in terms of real fashion sales data, which forecast the total sales amounts of various item categories and cities on a monthly, quarterly or annual basis. 

Exterrsive experiments were condircted to validate the proposed HI model in terms of real fashion retail data The experimental resirlts have shown that the HI model can tackle the medium-term sales forecasting problem effectively, which also demorrstrates that the proposed model can provide much superior performance over traditional ARIM A models and two recently developed sales forecasting NN models. A further experiment was presented based on seven irregular annual data sets from M3 competitiort, which further validates the effectiveness of the proposed HI model and shows that the HI model is more powerful to tackle the time series with sufficient sample data. Firrthermore, since the time series tackled in this chapter involve various patterns such as irregirlarity and seasonality, the proposed model is widely applicable and can be easily extended to solve other forecasting problems with similar time-series patterns. 

The proposed model provides forecasts based only on historical sales data, which cannot reflect the effects of exogenoits factors, such as weather and economic indexes, on fashion sales. Futirre research will focus on investigating mirltivariate HI forecasting models cortsidering the effects of various exogenous changes on fashion sales. Moreover, it is also a worthwhile research direction to... 

Sun, Z., Choi, T, Au, K. and Yu, Y, 2008. Sales forecasting using extreme learning machine with applications in fashion retailing. Decision Support Systems, 46(1), 411-419. 

The fashion retail business is characterized by short product life cycles, volatile customer demands and tremendous product varieties. Most fashion items are of strong seasonality. Uncertain customer demands in a frequently changing market envirorunent and nitmerous explanatory variables that influence fashion sales caitse an increase in irregularity or randomicity of sales data. Such distinct characteristics increase the complexity of sales forecasting in the fashion retail industry. For most fashion products, market demand is rmcertain until the selling season has started. When the actual demand deviates from the forecast, fashion retailers may not have time to respond to changes. Stock outages may occur for certain styles or sizes of fashion products and thus affect the profitability for fashion retailers. 

Wong, W.K., Guo, Z.X., 2010. A hybrid inteUigent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm. Int. J. Prod. Econ. 128, 614-624. 

Yu, Y., Choi, T.M., Hui, C.L., 2011. An intelligent fast sales forecasting model for fashion products. Expert Syst. Appl. 38, 7373—7379. 

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