In this study, an analysis of the time series was conducted using a class of shift functions for arithmetic and geometric progressions, along with their synchronization using logarithmic decrement. The closing prices of IBM company stocks were taken as the examined data for each trading day. The shift functions of geometric and arithmetic progressions revealed almost-proportions and almost-periods in the examined data. These detected patterns emphasize the importance of applying shift functions in the analysis of time series, allowing the extraction of internal patterns and periodic fluctuations that might go unnoticed with standard analysis methods. Computing the minima and corresponding values of the geometric progression enabled the identification of almost-periods in the data. These results not only confirmed visual observations but also enhanced our understanding of the internal patterns of the time series. The findings underscore the effectiveness of applying methods for analyzing time series based on almost-proportions and metric techniques. These approaches play a crucial role in uncovering hidden patterns and subtle periodicities in data, providing a fundamental foundation for more accurate analysis and successful forecasting.

Keywords: nearly-proportionalities, synchronization of geometric progression, empirical data, geometric progression, shift functions