W.K. Wong and Z.X. Guo (PR China)
Seasonal product demand forecasting, harmony search, neural network, and extreme learning machine
To handle the seasonal product demand forecasting problem, this paper develops an intelligent forecasting model integrating a harmony search algorithm, an extreme learning machine (ELM) and a heuristic fine-tuning process. The ELM combined with harmony search algorithm is used to provide effective initial forecasts. The heuristic fine-tuning process is then used to generate more accurate forecasts based on the initial forecasts. Extensive experiments based on real fashion retail data were conducted to evaluate the performance of the proposed model. The experimental results demonstrate that the performance of the proposed model is much superior to two recently developed neural network models for seasonal product demand forecasting.
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