The article discusses how to break down a time series into different sinusoidal components to better understand its behavior. This process, known as spectral analysis, helps in identifying important frequencies in the data. By looking at the frequency domain, we can gain insights that may not be apparent in the time domain. This approach is useful in various fields, such as designing structures to withstand specific vibrations or analyzing multivariate processes. The researchers introduce the concept of spectral density to describe the frequency breakdown of the data and use the periodogram to estimate this density. Additionally, they explore how linear filters can be understood from a spectral perspective, providing a new way to analyze processes like ARMA models.