基于CBR的化妆品销售组合预测模型

预测是销售企业面临的至关重要的问题,预测精度会直接影响到企业的利润及长远发展。考虑到化妆品销售独特的生命周期特点,提出基于案例推理技术的化妆品销售组合预测模型。基于案例推理技术能够弥补短生命周期产品数据不足的问题,根据"最相似"产品的数据对目标产品属性进行推理计算,既提高了预测精度,又便于实际操作。在此基础上,提出了改进的BASS模型及季节性趋势增长模型,充分考虑数据序列波动的季节性、趋势性及其完整的生命周期特征。为了进一步提高预测精度,提出AFTER-GA组合预测算法,并添加PSOWv-SVM模型作为组合预测的模型之一,利用模型处理外界影响因素的能力,使得各模型取长补短,更有效地突出各模型的优势,从而取得更精确的预测结果。实例分析表明,AFTER-GA组合预测算法在精度上要明显优于其他方法;基于CBR的组合预测模型在化妆品销售预测方面,能够达到令人满意的精度要求,具有实际应用价值。

Cosmetics Sales Combination Forecasting Based on CBR

Forecast is critical to sales companies, and its accuracy will directly affect profits and long-term development. Considering the unique life cycle features of cosmetics sales, the combination forecasting model based on CBR (Case-Based Reasoning) is proposed. The CBR can compensate for insufficient data of short life cycle products, and reasoning and calculating the targets' attributes according to the "most similar" are not only more accurate, but also easy to practice. On this basis, the paper puts foreword the improved BASS model and seasonal trends growth model, giving full consideration to seasonal and trend fluctuations in the data series, as well as its complete life cycle characteristics. And then, the AFTER-GA (Aggregated Forecast Through Exponential Re-weighting-Genetic Algorithm) is proposed, taking PSOWv-SVM for its ability to deal with external factors, highlighting the strengths of each model, and so to obtain higher accuracy. Example shows that the AFTER-GA is superior to other methods in accuracy; meanwhile, the combination forecasting model based on CBR can achieve a satisfactory accuracy, and has practical value for cosmetics sales forecast.