<![CDATA[Analyzing high-dimensional data often presents unique challenges, necessitating dimension reduction methods, one of which is the wavelet approach. Wavelets function as a transformation that automatically separates data into several components, then analyzes each component based on a resolution appropriate to its time scale. The Continuous Wavelet Transform (CWT) is a dimension reduction technique that relies on multiresolution decomposition to address modeling problems by generating local signal representations in the time and frequency domains (scales) continuously. Through multiresolution decomposition, trends in time series data can be separated. This transformation enables the transfer of data from the original domain into the wavelet domain for further analysis, as well as facilitating the separation of signals at both low and high frequencies more accurately. This study revisits the use of CWT, which divides data into various scales or frequency components and analyzes each part with the appropriate resolution. In this context, the Morlet wavelet filter is used. The results of the study indicate that the Morlet wavelet in CWT has advantages in detecting transient frequency components and local oscillation phenomena, making it highly effective in analyzing complex signals.]]>
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