APPLICATION OF FOURIER TRANSFORM FOR IDENTIFYING SEASONAL FLUCTUATIONS IN CONSUMER DEMAND IN RETAIL TRADE
Keywords:
Fourier transform, time series analysis, demand seasonality, retail trade, sales forecasting, spectral analysis, consumer demandAbstract
This article examines the application of the Fourier transform for analyzing seasonal fluctuations in consumer demand in retail trade. Seasonality is one of the key factors affecting sales dynamics across various product categories. Traditional time series analysis methods allow researchers to identify trends and seasonal components; however, their effectiveness may decrease when demand patterns contain complex periodic dependencies. The Fourier transform makes it possible to decompose a sales time series into a set of harmonic components and determine dominant frequencies that characterize consumer purchasing behavior. The paper presents the theoretical foundations of the method, an analysis algorithm, and an example of its application to retail sales data. The results demonstrate that spectral analysis can effectively identify weekly, monthly, and annual demand cycles, thereby improving forecasting accuracy and inventory management efficiency. The study confirms the potential of Fourier-based methods as a tool for demand forecasting and decision support in modern retail management.
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