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.

翻译：营销是确保我们的销售在市场上达到最优、价格最易于接受、客户满意度最高的方式，从而保证品牌获得最广泛的覆盖。这需要对整个相关网络具备复杂且深入的理解。事实上，营销数据可能以不同形式存在，例如定性数据和定量数据。然而，在文献中容易注意到，关于定性研究可收集到大量参考文献，而采用定量视角的研究却寥寥无几。这是一个重大缺陷，导致营销科学仍聚焦于设计层面，尽管市场强烈依赖于金钱和时间等数量因素。实际上，营销数据可构成时间序列，例如特定时期内的品牌销售额、特定时期内的品牌相关价格、市场份额等。本研究的目的是基于时间序列探讨多种品牌的一些营销模型。本文旨在结合动态模态分解与小波分解，研究同时包含价格和销量数据的营销序列，以根据品牌特征及相关市场竞争或竞争对手情况，探索时间尺度对品牌销售在市场中持续性的影响以及对这种持续性的预测。我们的研究基于2017年11月22日至2021年12月30日期间沙特品牌的一个样本。