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The method for identifying a pattern that is constantly changing in Python would depend on the specific situation and data being analyzed. However, some common methods for identifying patterns in time-series data that are constantly changing include:

Moving Averages: This method involves calculating the average value of a variable over a specific period of time, and then plotting those values over time. By smoothing out the data this way, it can help to identify trends and patterns that may be obscured by random fluctuations.
Fourier Transform: This method is used to decompose a signal into its underlying frequency components. By analyzing the spectrum of frequencies, it can help to identify repeating patterns or cycles in the data.
Wavelet Analysis: This method is similar to Fourier analysis, but it allows for the detection of changes in frequency over time. This can help to identify patterns that are constantly changing in frequency, such as those seen in weather or financial data.
Autocorrelation: This method involves analyzing the correlation between a variable and its past values. By looking at how the variable behaves over time, it can help to identify patterns and trends that may not be apparent from a single snapshot of the data.