When analysing short-term economic and financial data, it is important to consider phenomena such as economic cycles, trends, and their changes, as well as structural shifts and financial bubbles. This study proposes an effective tool for visualizing these phenomena for high-frequency data, which refers to data collected on a weekly, daily, or more frequent basis. The method involves several steps. First, the choice of a time interval, the change within which is insignificant for analysis purposes. Next, the time series is smoothed using a filter that ignores these intervals. Then, the increments of the time series are calculated by taking differences between neighbouring values. Finally, a phase portrait is constructed by projecting the data onto a two-dimensional plane, with the values of the series on the x-axis and their increments on the y-axis. Geometric properties of these phase portraits can be used to identify features and variations in the time series. This approach allows for a better understanding of short-term trends and fluctuations in economic and financial indicators.