Multifractal wavelet dynamic mode decomposition modeling for marketing time series
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营销时间序列的多重分形小波动态模态分解建模
- 研究问题
- 品牌价格与销量的时间序列中，时间尺度如何影响销售持续性及其预测，品牌特征和市场竞争又如何参与这一分析？
- 方法与思路
- 作者结合动态模态分解与小波分解，分析品牌价格和销量序列，使用二〇一七年十一月至二〇二一年十二月的沙特品牌样本。
- 材料陈述的贡献
- 摘要明确提出将两种分解方法结合以研究销售持续性及其预测，并说明样本范围；摘要未报告具体发现或相对其他方法的改进。
- 阅读关注点
- 应核对多重分形与各分解步骤的定义、品牌样本和数据频率、持续性的衡量方式，以及预测检验是否包含样本外比较。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核实；摘要未说明模型细节、预测精度或实证结论，不能据此判断营销策略或预测优势。
原文摘要
Marketing is the way we ensure our sales are the best in the market, our prices the most accessible, and our clients satisfied, thus ensuring our brand has the widest distribution. This requires sophisticated and advanced understanding of the whole related network. Indeed, marketing data may exist in different forms such as qualitative and quantitative data. However, in the literature, it is easily noted that large bibliographies may be collected about qualitative studies, while only a few studies adopt a quantitative point of view. This is a major drawback that results in marketing science still focusing on design, although the market is strongly dependent on quantities such as money and time. Indeed, marketing data may form time series such as brand sales in specified periods, brand-related prices over specified periods, market shares, etc. The purpose of the present work is to investigate some marketing models based on time series for various brands. This paper aims to combine the dynamic mode decomposition and wavelet decomposition to study marketing series due to both prices, and volume sales in order to explore the effect of the time scale on the persistence of brand sales in the market and on the forecasting of such persistence, according to the characteristics of the brand and the related market competition or competitors. Our study is based on a sample of Saudi brands during the period 22 November 2017 to 30 December 2021.