Fourier-Multiple Linear Regression Method in the Fast-Moving Consumer Goods Business Time Series Analysis Problem

A new method is developed to adjust the forecast in case of a small number of observations or in the absence of standard patterns which can be detected by classical machine learning and statistical methods. The effectiveness is demonstrated in comparison with existing methods in case of a large number of additional features and the presence of seasonal components.