Forecasting brand sales with wavelet decompositions of related causal series
Forecasting brand sales with wavelet decompositions of related causal series
复制标题
利用相关因果序列的小波分解预测品牌销量
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Antonis A. Michis
中科院分区：
文献类型：
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作者：
Antonis A. Michis
我们考虑利用相关因果序列的小波分解预测品牌销售的方法。小波分解可以发现隐藏的周期性内在的营销时间序列，如定价，因此可以提供上级信息的因果销售预测方法。我们专门解决多重共线性的问题，因为所提出的长度为T的时间序列的小波包变换，生成2 T- 2个相关的系数向量，每个长度为T。我们发现，偏最小二乘法提供了最准确的预测方法，在同一时间实现所需的降维估计问题。
We consider methods for forecasting brand sales utilising wavelet decompositions of related causal series. Wavelet decompositions can uncover the hidden periodicities inherent in marketing time series like pricing and can therefore provide superior information in causal sales forecasting methods. We specifically address the problem of multicollinearity since the proposed wavelet packet transformation of a time series of length T, generates 2T – 2 correlated vectors of coefficients, each of length T. We find that partial least-squares provide the most accurate forecasting method which at the same time achieves the desired dimension reduction in the estimation problem.